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# Reload the module to pick up the new method
import importlib
import provesid.zeropm
importlib.reload(provesid.zeropm)
from provesid.zeropm import ZeroPM

# Initialize the ZeroPM database connection
zpm = ZeroPM()
print("✓ ZeroPM reloaded and initialized successfully!")
# Reload the module to pick up the new method
import importlib
import provesid.zeropm
importlib.reload(provesid.zeropm)
from provesid.zeropm import ZeroPM

# Initialize the ZeroPM database connection
zpm = ZeroPM()
print("✓ ZeroPM reloaded and initialized successfully!")

✓ ZeroPM reloaded and initialized successfully!

ZeroPM Tutorial¶

This notebook demonstrates the functionality of the ZeroPM class, which provides efficient access to the ZeroPM SQLite database containing chemical identifiers and properties.

Overview¶

ZeroPM allows you to:

Query chemicals by CAS number or name
Query chemicals by regulatory inventory, country, or region
Convert between different chemical identifiers (CAS, InChI, InChIKey, SMILES)
Perform fuzzy name searches
Batch process multiple chemicals
Search by substructure
Export results to CSV

Database Auto-Download¶

The ZeroPM database (~400MB) is automatically downloaded from GitHub on first use. The download happens once and the database is cached locally for future use.

Let's get started!

1. Installation and Setup¶

First, make sure you have the required dependencies installed.

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# Import the ZeroPM class
from provesid.zeropm import ZeroPM

# Initialize the ZeroPM database connection
zpm = ZeroPM()

print("✓ ZeroPM initialized successfully!")
print(f"Database path: {zpm.db_path}")
# Import the ZeroPM class
from provesid.zeropm import ZeroPM

# Initialize the ZeroPM database connection
zpm = ZeroPM()

print("✓ ZeroPM initialized successfully!")
print(f"Database path: {zpm.db_path}")

✓ ZeroPM initialized successfully!
Database path: /home/ali/projects/USETOX/PROVESID/src/provesid/data/zeropm-v0-0-4.sqlite

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# playing around with the ZeroPM instance
query_id = zpm.query_cas("50-00-0")
inchi_ids, ranks = zpm.get_inchi_id(query_id)
print(f"InChI IDs for CAS 50-00-0: {inchi_ids} with ranks {ranks}")
# playing around with the ZeroPM instance
query_id = zpm.query_cas("50-00-0")
inchi_ids, ranks = zpm.get_inchi_id(query_id)
print(f"InChI IDs for CAS 50-00-0: {inchi_ids} with ranks {ranks}")

InChI IDs for CAS 50-00-0: [32227] with ranks [1]

Testing the new get_id_table_from_cas method¶

This method returns a pandas DataFrame with all identifiers (CAS, InChI, InChIKey, synonyms) for a given CAS number.

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# Get identifier table for formaldehyde (CAS: 50-00-0)
df = zpm.get_id_table_from_cas("50-00-0")
print(df)
print(f"\nNumber of rows: {len(df)}")
# Get identifier table for formaldehyde (CAS: 50-00-0)
df = zpm.get_id_table_from_cas("50-00-0")
print(df)
print(f"\nNumber of rows: {len(df)}")

       cas  query_id  inchi_id  rank                    inchi  \
0  50-00-0      8671     32227     1  InChI=1S/CH2O/c1-2/h1H2   

                      inchikey  zeropm_id  \
0  WSFSSNUMVMOOMR-UHFFFAOYSA-N       3224   

                                            synonyms  \
0  formaldehyde ...%; FORMALIN; Formaldehyde; for...   

                                             sources  
0  Chemical Data Reporting Inventory, Industrial ...  

Number of rows: 1

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# Display the full table with better formatting
print("\nFull table with all columns:")
print(df.to_string())

# Show specific columns
print("\n\nKey columns:")
print(df[['cas', 'inchi', 'inchikey']].to_string())
# Display the full table with better formatting
print("\nFull table with all columns:")
print(df.to_string())

# Show specific columns
print("\n\nKey columns:")
print(df[['cas', 'inchi', 'inchikey']].to_string())

Full table with all columns:
       cas  query_id  inchi_id  rank                    inchi                     inchikey  zeropm_id                                                                               synonyms                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          sources
0  50-00-0      8671     32227     1  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N       3224  formaldehyde ...%; FORMALIN; Formaldehyde; formaldehyde ... %; Formalin; formaldehyde  Chemical Data Reporting Inventory, Industrial Processing and Use; Chemical Data Reporting Inventory, Consumer and Commercial Use; Chemical Data Reporting Inventory, Manufacturing-Import Information; Chemical Data Reporting Inventory, Nationally Aggregated Production Volumes; Industrial Chemicals Inventory; Inventory of Existing Chemical Substances and Chemicals; Inventory of Chemicals and Chemical Substances; NITE; Hazardous Chemical Information System; Substances in Preparations in Nordic Countries Inventory; CLP Annex VI; Chemical Information Management System Inventory;  Chemicals Information System ; Chemical Substance Inventory; Chemical Substances Control Law Existing Chemical Substances; Inventory of Chemicals; EC Inventory; Toxic Substances Control Act (TSCA) Chemical Substance Inventory; Domestic Substance List


Key columns:
       cas                    inchi                     inchikey
0  50-00-0  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N

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# Test with another compound that might have multiple entries
# Let's try with aspirin
df2 = zpm.get_id_table_from_cas("50-78-2")  # Aspirin
if df2 is not None:
    print("\nAspirin (CAS: 50-78-2):")
    print(df2[['cas', 'inchi_id', 'rank', 'inchikey']].to_string())
else:
    print("\nAspirin not found in database")
# Test with another compound that might have multiple entries
# Let's try with aspirin
df2 = zpm.get_id_table_from_cas("50-78-2")  # Aspirin
if df2 is not None:
    print("\nAspirin (CAS: 50-78-2):")
    print(df2[['cas', 'inchi_id', 'rank', 'inchikey']].to_string())
else:
    print("\nAspirin not found in database")

Aspirin (CAS: 50-78-2):
       cas  inchi_id  rank                     inchikey
0  50-78-2     45333     1  BSYNRYMUTXBXSQ-UHFFFAOYSA-N
1  50-78-2    182692     2  BSYNRYMUTXBXSQ-UHFFFAOYSA-M
2  50-78-2      9014     3  XDZMPRGFOOFSBL-UHFFFAOYSA-N
3  50-78-2    208438     4  BSYNRYMUTXBXSQ-FIBGUPNXSA-N

Benefits of get_id_table_from_cas¶

The get_id_table_from_cas() method provides several advantages:

Comprehensive identifier retrieval: Returns CAS, InChI, InChIKey, and synonyms in one call
Handles multiple structures: Some CAS numbers map to multiple InChI structures (different forms, salts, etc.)
Rank information: Includes rank to indicate the relevance/confidence of each match
Easy data manipulation: Returns a pandas DataFrame for easy filtering, sorting, and export

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df2
df2

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	cas	query_id	inchi_id	rank	inchi	inchikey	zeropm_id	synonyms	sources
0	50-78-2	11272	45333	1	InChI=1S/C9H8O4/c1-6(10)13-8-5-3-2-4-7(8)9(11)...	BSYNRYMUTXBXSQ-UHFFFAOYSA-N	4267	2-acetoxybenzoic acid; Acetylsalicyclic acid; ...	Substances in Preparations in Nordic Countries...
1	50-78-2	11272	182692	2	InChI=1S/C9H8O4/c1-6(10)13-8-5-3-2-4-7(8)9(11)...	BSYNRYMUTXBXSQ-UHFFFAOYSA-M	<NA>	2-acetoxybenzoic acid; Acetylsalicyclic acid; ...	Substances in Preparations in Nordic Countries...
2	50-78-2	11272	9014	3	InChI=1S/C9H10O3/c1-2-12-8-6-4-3-5-7(8)9(10)11...	XDZMPRGFOOFSBL-UHFFFAOYSA-N	6402	2-acetoxybenzoic acid; Acetylsalicyclic acid; ...	Substances in Preparations in Nordic Countries...
3	50-78-2	11272	208438	4	InChI=1S/C9H8O4/c1-6(10)13-8-5-3-2-4-7(8)9(11)...	BSYNRYMUTXBXSQ-FIBGUPNXSA-N	<NA>	2-acetoxybenzoic acid; Acetylsalicyclic acid; ...	Substances in Preparations in Nordic Countries...

Batch Processing Multiple CAS Numbers¶

For processing multiple CAS numbers at once, use batch_get_id_table_from_cas():

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# Process multiple CAS numbers at once
cas_list = ["50-00-0", "50-78-2", "64-17-5"]  # formaldehyde, aspirin, ethanol

batch_df = zpm.batch_get_id_table_from_cas(cas_list)

print(f"Total rows: {len(batch_df)}")
print(f"\nNumber of structures per CAS:")
print(batch_df.groupby('cas').size())
print(f"\nFirst few rows:")
print(batch_df[['cas', 'inchi_id', 'rank', 'inchikey']].head(10))
# Process multiple CAS numbers at once
cas_list = ["50-00-0", "50-78-2", "64-17-5"]  # formaldehyde, aspirin, ethanol

batch_df = zpm.batch_get_id_table_from_cas(cas_list)

print(f"Total rows: {len(batch_df)}")
print(f"\nNumber of structures per CAS:")
print(batch_df.groupby('cas').size())
print(f"\nFirst few rows:")
print(batch_df[['cas', 'inchi_id', 'rank', 'inchikey']].head(10))

Total rows: 6

Number of structures per CAS:
cas
50-00-0    1
50-78-2    4
64-17-5    1
dtype: int64

First few rows:
       cas  inchi_id  rank                     inchikey
0  50-00-0     32227     1  WSFSSNUMVMOOMR-UHFFFAOYSA-N
1  50-78-2     45333     1  BSYNRYMUTXBXSQ-UHFFFAOYSA-N
2  50-78-2    182692     2  BSYNRYMUTXBXSQ-UHFFFAOYSA-M
3  50-78-2      9014     3  XDZMPRGFOOFSBL-UHFFFAOYSA-N
4  50-78-2    208438     4  BSYNRYMUTXBXSQ-FIBGUPNXSA-N
5  64-17-5     60355     1  LFQSCWFLJHTTHZ-UHFFFAOYSA-N

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# The batch method handles missing CAS numbers gracefully
mixed_cas_list = ["50-00-0", "999-99-9", "50-78-2"]  # valid, invalid, valid

batch_df_mixed = zpm.batch_get_id_table_from_cas(mixed_cas_list)
print(f"Requested {len(mixed_cas_list)} CAS numbers")
print(f"Found {len(batch_df_mixed['cas'].unique())} in database")
print(f"CAS numbers found: {list(batch_df_mixed['cas'].unique())}")
# The batch method handles missing CAS numbers gracefully
mixed_cas_list = ["50-00-0", "999-99-9", "50-78-2"]  # valid, invalid, valid

batch_df_mixed = zpm.batch_get_id_table_from_cas(mixed_cas_list)
print(f"Requested {len(mixed_cas_list)} CAS numbers")
print(f"Found {len(batch_df_mixed['cas'].unique())} in database")
print(f"CAS numbers found: {list(batch_df_mixed['cas'].unique())}")

WARNING:root:CAS number 999-99-9 not found in database

Requested 3 CAS numbers
Found 2 in database
CAS numbers found: ['50-00-0', '50-78-2']

2. Database Statistics¶

Let's start by exploring what's in the database.

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# Get database statistics
stats = zpm.get_database_stats()

print("Database Statistics:")
print("=" * 50)
for key, value in stats.items():
    print(f"{key:30s}: {value:>15,}" if isinstance(value, int) else f"{key:30s}: {value}")
# Get database statistics
stats = zpm.get_database_stats()

print("Database Statistics:")
print("=" * 50)
for key, value in stats.items():
    print(f"{key:30s}: {value:>15,}" if isinstance(value, int) else f"{key:30s}: {value}")

Database Statistics:
==================================================
api_ready_query               :         447,617
api_results                   :       1,349,825
substances                    :         359,221
inventories                   :         543,851
inventory_summary             :       1,045,887
cleanventory_chemicals        :         483,388
zeropm_chemicals              :         126,369
components                    :          22,380
multi_components              :          38,696
unique_cas_numbers            :         164,513
unique_chemical_names         :         283,104

3. Querying by CAS Number¶

The most common way to query the database is using a CAS Registry Number.

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# Get a sample CAS number from the database
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'CAS Registry Number' 
    LIMIT 1
""")
sample_cas = zpm.cursor.fetchone()[0]

print(f"Sample CAS number: {sample_cas}")

# Query the CAS number to get a query_id
query_id = zpm.query_cas(sample_cas)
print(f"Query ID: {query_id}")

# Get InChI information
inchi_ids, ranks = zpm.get_inchi_id(query_id)
if inchi_ids:
    inchi, inchikey = zpm.get_inchi(inchi_ids[0])
    print(f"\nInChI: {inchi[:50]}..." if len(inchi) > 50 else f"\nInChI: {inchi}")
    print(f"InChIKey: {inchikey}")
# Get a sample CAS number from the database
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'CAS Registry Number' 
    LIMIT 1
""")
sample_cas = zpm.cursor.fetchone()[0]

print(f"Sample CAS number: {sample_cas}")

# Query the CAS number to get a query_id
query_id = zpm.query_cas(sample_cas)
print(f"Query ID: {query_id}")

# Get InChI information
inchi_ids, ranks = zpm.get_inchi_id(query_id)
if inchi_ids:
    inchi, inchikey = zpm.get_inchi(inchi_ids[0])
    print(f"\nInChI: {inchi[:50]}..." if len(inchi) > 50 else f"\nInChI: {inchi}")
    print(f"InChIKey: {inchikey}")

Sample CAS number: 121-20-0
Query ID: 1

InChI: InChI=1S/C21H28O5/c1-7-8-9-14-13(3)17(11-16(14)22)...
InChIKey: SHCRDCOTRILILT-WOBDGSLYSA-N

4. Converting CAS to SMILES¶

ZeroPM can convert CAS numbers to SMILES using RDKit.

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# Get SMILES from CAS number
smiles = zpm.get_smiles_from_cas(sample_cas)
print(f"CAS: {sample_cas}")
print(f"SMILES: {smiles}")

# Get chemical names
names = zpm.get_names(sample_cas)
if names:
    print(f"\nAlternative names ({len(names)}):")
    for i, name in enumerate(names[:5], 1):  # Show first 5 names
        print(f"  {i}. {name}")
    if len(names) > 5:
        print(f"  ... and {len(names) - 5} more")
# Get SMILES from CAS number
smiles = zpm.get_smiles_from_cas(sample_cas)
print(f"CAS: {sample_cas}")
print(f"SMILES: {smiles}")

# Get chemical names
names = zpm.get_names(sample_cas)
if names:
    print(f"\nAlternative names ({len(names)}):")
    for i, name in enumerate(names[:5], 1):  # Show first 5 names
        print(f"  {i}. {name}")
    if len(names) > 5:
        print(f"  ... and {len(names) - 5} more")

CAS: 121-20-0
SMILES: C/C=C\CC1=C(C)[C@@H](OC(=O)[C@@H]2[C@@H](/C=C(\C)C(=O)OC)C2(C)C)CC1=O

Alternative names (7):
  1. Cyclopropanecarboxylic acid, 3-(3-methoxy-2-methyl-3-oxo-1-propenyl)-2,2-dimethyl-, 3-(2-butenyl)-2-methyl-4-oxo-2-cyclopenten-1-yl ester, [1R-[1.alpha.[S*(Z)],3.beta.(E)]]-
  2. CINERIN II
  3. 3-(but-2-enyl)-2-methyl-4-oxocyclopent-2-enyl2,2-dimethyl-3-(3-methoxy-2-methyl-3-oxoprop-1-enyl)cyclopropanecarboxylate
  4. Jasmolin II
  5. Cyclopropanecarboxylic acid, 3-[(1E)-3-methoxy-2-methyl-3-oxo-1-propenyl]-2,2-dimethyl-, (1S)-3-(2Z)-2-butenyl-2-methyl-4-oxo-2-cyclopenten-1-yl ester, (1R,3R)-
  ... and 2 more

5. Querying by Chemical Name¶

You can search for chemicals by their exact name or use fuzzy matching.

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# Get a sample chemical name
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'chemical name' 
    LIMIT 1
""")
sample_name = zpm.cursor.fetchone()[0]

# Exact match
query_id = zpm.query_name(sample_name)
print(f"Exact search for '{sample_name}'")
print(f"Query ID: {query_id}")

# Fuzzy match (with partial name)
if len(sample_name) >= 5:
    partial_name = sample_name[:5]
    print(f"\nFuzzy search for '{partial_name}':")
    similar_ids = zpm.query_similar_name(partial_name, number_of_results=5, score_cutoff=70)
    if similar_ids:
        print(f"Found {len(similar_ids)} similar matches")
        for qid in similar_ids[:3]:
            zpm.cursor.execute("SELECT query FROM api_ready_query WHERE query_id = ?", (qid,))
            result = zpm.cursor.fetchone()
            if result:
                print(f"  - {result[0]}")
# Get a sample chemical name
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'chemical name' 
    LIMIT 1
""")
sample_name = zpm.cursor.fetchone()[0]

# Exact match
query_id = zpm.query_name(sample_name)
print(f"Exact search for '{sample_name}'")
print(f"Query ID: {query_id}")

# Fuzzy match (with partial name)
if len(sample_name) >= 5:
    partial_name = sample_name[:5]
    print(f"\nFuzzy search for '{partial_name}':")
    similar_ids = zpm.query_similar_name(partial_name, number_of_results=5, score_cutoff=70)
    if similar_ids:
        print(f"Found {len(similar_ids)} similar matches")
        for qid in similar_ids[:3]:
            zpm.cursor.execute("SELECT query FROM api_ready_query WHERE query_id = ?", (qid,))
            result = zpm.cursor.fetchone()
            if result:
                print(f"  - {result[0]}")

Exact search for 'cinerin II'
Query ID: 2

Fuzzy search for 'ciner':

Found 5 similar matches
  - cinerin II
  - cinerin I
  - ne

6. Converting Between Different Identifiers¶

ZeroPM supports conversion between various chemical identifiers.

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# Get a sample InChI and InChIKey
zpm.cursor.execute("""
    SELECT inchi, inchikey 
    FROM substances 
    LIMIT 1
""")
test_inchi, test_inchikey = zpm.cursor.fetchone()

print("Identifier Conversions:")
print("=" * 60)

# InChI to CAS
cas_from_inchi = zpm.get_cas_from_inchi(test_inchi)
print(f"InChI → CAS: {cas_from_inchi}")

# InChIKey to CAS
cas_from_key = zpm.get_cas_from_inchikey(test_inchikey)
print(f"InChIKey → CAS: {cas_from_key}")

# InChIKey to SMILES
smiles_from_key = zpm.get_smiles_from_inchikey(test_inchikey)
print(f"InChIKey → SMILES: {smiles_from_key}")

# SMILES to CAS (if we have a valid SMILES)
if smiles_from_key:
    cas_from_smiles = zpm.get_cas_from_smiles(smiles_from_key)
    print(f"SMILES → CAS: {cas_from_smiles}")
# Get a sample InChI and InChIKey
zpm.cursor.execute("""
    SELECT inchi, inchikey 
    FROM substances 
    LIMIT 1
""")
test_inchi, test_inchikey = zpm.cursor.fetchone()

print("Identifier Conversions:")
print("=" * 60)

# InChI to CAS
cas_from_inchi = zpm.get_cas_from_inchi(test_inchi)
print(f"InChI → CAS: {cas_from_inchi}")

# InChIKey to CAS
cas_from_key = zpm.get_cas_from_inchikey(test_inchikey)
print(f"InChIKey → CAS: {cas_from_key}")

# InChIKey to SMILES
smiles_from_key = zpm.get_smiles_from_inchikey(test_inchikey)
print(f"InChIKey → SMILES: {smiles_from_key}")

# SMILES to CAS (if we have a valid SMILES)
if smiles_from_key:
    cas_from_smiles = zpm.get_cas_from_smiles(smiles_from_key)
    print(f"SMILES → CAS: {cas_from_smiles}")

Identifier Conversions:
============================================================
InChI → CAS: ['100-00-5', '68239-23-6']
InChIKey → CAS: ['100-00-5', '68239-23-6']
InChIKey → SMILES: O=[N+]([O-])c1ccc(Cl)cc1
SMILES → CAS: ['100-00-5', '68239-23-6']

[12:26:29] WARNING: Charges were rearranged

7. Batch Processing¶

For efficiency, ZeroPM provides batch methods to process multiple chemicals at once.

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# Get multiple CAS numbers
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'CAS Registry Number' 
    LIMIT 5
""")
cas_list = [row[0] for row in zpm.cursor.fetchall()]

print(f"Batch processing {len(cas_list)} CAS numbers:")
print("=" * 60)

# Batch query CAS numbers
query_ids = zpm.batch_query_cas(cas_list)
for cas, qid in query_ids.items():
    print(f"{cas}: Query ID = {qid}")

print("\n" + "=" * 60)

# Batch get SMILES
smiles_dict = zpm.batch_get_smiles_from_cas(cas_list)
for cas, smiles in smiles_dict.items():
    print(f"{cas}: {smiles if smiles else 'N/A'}")
# Get multiple CAS numbers
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'CAS Registry Number' 
    LIMIT 5
""")
cas_list = [row[0] for row in zpm.cursor.fetchall()]

print(f"Batch processing {len(cas_list)} CAS numbers:")
print("=" * 60)

# Batch query CAS numbers
query_ids = zpm.batch_query_cas(cas_list)
for cas, qid in query_ids.items():
    print(f"{cas}: Query ID = {qid}")

print("\n" + "=" * 60)

# Batch get SMILES
smiles_dict = zpm.batch_get_smiles_from_cas(cas_list)
for cas, smiles in smiles_dict.items():
    print(f"{cas}: {smiles if smiles else 'N/A'}")

Batch processing 5 CAS numbers:
============================================================
121-20-0: Query ID = 1
25646-71-3: Query ID = 4
76823-93-3: Query ID = 9
177964-68-0: Query ID = 11
27955-94-8: Query ID = 14

============================================================

121-20-0: C/C=C\CC1=C(C)[C@@H](OC(=O)[C@@H]2[C@@H](/C=C(\C)C(=O)OC)C2(C)C)CC1=O
25646-71-3: CCN(CCNS(C)(=O)=O)c1ccc(N)c(C)c1.CCN(CCNS(C)(=O)=O)c1ccc(N)c(C)c1.O=S(=O)(O)O.O=S(=O)(O)O.O=S(=O)(O)O
76823-93-3: N#CCCSCc1csc(NC(=N)N)n1
177964-68-0: COCc1c(C(C)C)nc(C(C)C)c(C=CC=O)c1-c1ccc(F)cc1
27955-94-8: CC(c1ccc(O)cc1)(c1ccc(O)cc1)c1ccc(O)cc1

8. Batch InChIKey to CAS Conversion¶

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# Get multiple InChIKeys
zpm.cursor.execute("""
    SELECT inchikey 
    FROM substances 
    LIMIT 5
""")
inchikey_list = [row[0] for row in zpm.cursor.fetchall()]

print(f"Batch converting {len(inchikey_list)} InChIKeys to CAS:")
print("=" * 60)

cas_dict = zpm.batch_get_cas_from_inchikey(inchikey_list)
for key, cas in cas_dict.items():
    print(f"{key}: {cas if cas else 'N/A'}")
# Get multiple InChIKeys
zpm.cursor.execute("""
    SELECT inchikey 
    FROM substances 
    LIMIT 5
""")
inchikey_list = [row[0] for row in zpm.cursor.fetchall()]

print(f"Batch converting {len(inchikey_list)} InChIKeys to CAS:")
print("=" * 60)

cas_dict = zpm.batch_get_cas_from_inchikey(inchikey_list)
for key, cas in cas_dict.items():
    print(f"{key}: {cas if cas else 'N/A'}")

Batch converting 5 InChIKeys to CAS:
============================================================
: 71889-03-7
AAADGWUCZIMSKQ-UHFFFAOYSA-M: 16509-22-1
AAADKYXUTOBAGS-UHFFFAOYSA-N: 78-99-9
AAAFFJJBQGZTFF-UHFFFAOYSA-N: 5355-88-4
AAAFYYTUBLYGNG-UHFFFAOYSA-N: N/A

9. Advanced Search: Regex Pattern Matching¶

Search for chemicals using pattern matching.

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# Search for chemicals with names containing a pattern
# For example, search for names containing "acid"
pattern = "%acid%"

results = zpm.query_name_regex(pattern, case_sensitive=False, limit=10)

print(f"Chemical names matching pattern '{pattern}':")
print("=" * 60)
for query_id, name in results[:5]:
    print(f"  {name}")
print(f"\nTotal matches: {len(results)}")
# Search for chemicals with names containing a pattern
# For example, search for names containing "acid"
pattern = "%acid%"

results = zpm.query_name_regex(pattern, case_sensitive=False, limit=10)

print(f"Chemical names matching pattern '{pattern}':")
print("=" * 60)
for query_id, name in results[:5]:
    print(f"  {name}")
print(f"\nTotal matches: {len(results)}")

Chemical names matching pattern '%acid%':
============================================================
  2-chloroethylphosphonic acid
  Isocyanic acid, 2-methyl-m-phenylene ester
  3-(4-aminophenyl)-2-cyano-2-propenoic acid
  5-{4-[5-5-amino-2-[4-(2-sulfoxyethylsulfonyl)phenylazo]-4-sulfo-phenylamino]-6-chloro-1,3,5-triazin-2-ylamino}}-4-hydroxy-3-(1-sulfo-naphthalen-2-ylazo)-naphthalene-2,7-disulfonicacid sodium salt
  acetic acid ... %

Total matches: 10

10. Advanced Search: Substructure Search¶

Search for chemicals containing a specific substructure (SMARTS pattern).

⚠️ Note: This operation can be slow for large searches as it needs to check each molecule.

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# Search for molecules containing a benzene ring
smarts_pattern = "c1ccccc1"  # Benzene ring

print(f"Searching for molecules with benzene ring (max 5 results)...")
results = zpm.get_cas_by_substructure(smarts_pattern, max_results=5)

print(f"Found {len(results)} molecules with benzene ring:")
print("=" * 60)
for i, compound in enumerate(results, 1):
    print(f"\n{i}. CAS: {compound['cas']}")
    print(f"   SMILES: {compound['smiles']}")
    print(f"   InChIKey: {compound['inchikey'][:27]}...")
# Search for molecules containing a benzene ring
smarts_pattern = "c1ccccc1"  # Benzene ring

print(f"Searching for molecules with benzene ring (max 5 results)...")
results = zpm.get_cas_by_substructure(smarts_pattern, max_results=5)

print(f"Found {len(results)} molecules with benzene ring:")
print("=" * 60)
for i, compound in enumerate(results, 1):
    print(f"\n{i}. CAS: {compound['cas']}")
    print(f"   SMILES: {compound['smiles']}")
    print(f"   InChIKey: {compound['inchikey'][:27]}...")

Searching for molecules with benzene ring (max 5 results)...
Found 5 molecules with benzene ring:
============================================================

1. CAS: ['100-00-5', '68239-23-6']
   SMILES: O=[N+]([O-])c1ccc(Cl)cc1
   InChIKey: CZGCEKJOLUNIFY-UHFFFAOYSA-N...

2. CAS: ['100-01-6', '10040-98-9', '68239-24-7']
   SMILES: Nc1ccc([N+](=O)[O-])cc1
   InChIKey: TYMLOMAKGOJONV-UHFFFAOYSA-N...

3. CAS: ['100-02-7', '25154-55-6']
   SMILES: O=[N+]([O-])c1ccc(O)cc1
   InChIKey: BTJIUGUIPKRLHP-UHFFFAOYSA-N...

4. CAS: 100-03-8
   SMILES: O=S(O)c1ccc(Cl)cc1
   InChIKey: AOQYAMDZQAEDLO-UHFFFAOYSA-N...

5. CAS: ['100-04-9', '13533-17-0', '24564-52-1']
   SMILES: CN(C)c1ccc([N+]#N)cc1.[Cl-]
   InChIKey: CCIAVEMREXZXAK-UHFFFAOYSA-M...

11. Exporting Data to CSV¶

You can export query results to CSV files for further analysis.

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import tempfile
import os

# Create a temporary directory for exports
temp_dir = tempfile.mkdtemp()

# Export batch results
output_file = os.path.join(temp_dir, 'cas_smiles_export.csv')
zpm.export_to_csv(
    list(smiles_dict.items()), 
    output_file,
    columns=['CAS', 'SMILES']
)

print(f"✓ Exported data to: {output_file}")

# Export custom query results
sql_query = """
    SELECT aq.query AS CAS, s.inchikey
    FROM api_ready_query aq
    JOIN api_results ar ON aq.query_id = ar.query_id
    JOIN substances s ON ar.inchi_id = s.inchi_id
    WHERE aq.type = 'CAS Registry Number'
    LIMIT 10
"""
output_file2 = os.path.join(temp_dir, 'cas_inchikey_export.csv')
zpm.export_query_results(sql_query, output_file2, include_headers=True)

print(f"✓ Exported custom query to: {output_file2}")

# List exported files
print(f"\nExported files in {temp_dir}:")
for file in os.listdir(temp_dir):
    filepath = os.path.join(temp_dir, file)
    size = os.path.getsize(filepath)
    print(f"  - {file} ({size:,} bytes)")
import tempfile
import os

# Create a temporary directory for exports
temp_dir = tempfile.mkdtemp()

# Export batch results
output_file = os.path.join(temp_dir, 'cas_smiles_export.csv')
zpm.export_to_csv(
    list(smiles_dict.items()), 
    output_file,
    columns=['CAS', 'SMILES']
)

print(f"✓ Exported data to: {output_file}")

# Export custom query results
sql_query = """
    SELECT aq.query AS CAS, s.inchikey
    FROM api_ready_query aq
    JOIN api_results ar ON aq.query_id = ar.query_id
    JOIN substances s ON ar.inchi_id = s.inchi_id
    WHERE aq.type = 'CAS Registry Number'
    LIMIT 10
"""
output_file2 = os.path.join(temp_dir, 'cas_inchikey_export.csv')
zpm.export_query_results(sql_query, output_file2, include_headers=True)

print(f"✓ Exported custom query to: {output_file2}")

# List exported files
print(f"\nExported files in {temp_dir}:")
for file in os.listdir(temp_dir):
    filepath = os.path.join(temp_dir, file)
    size = os.path.getsize(filepath)
    print(f"  - {file} ({size:,} bytes)")

✓ Exported data to: /tmp/tmpc3_93yy2/cas_smiles_export.csv
✓ Exported custom query to: /tmp/tmpc3_93yy2/cas_inchikey_export.csv

Exported files in /tmp/tmpc3_93yy2:
  - cas_inchikey_export.csv (400 bytes)
  - cas_smiles_export.csv (353 bytes)

12. Performance Optimization: Creating Indexes¶

Create database indexes to speed up queries.

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# Create indexes for better query performance
print("Creating database indexes...")
index_results = zpm.create_indexes()

print("\nIndex Status:")
print("=" * 60)
for index_name, status in index_results.items():
    print(f"{index_name:30s}: {status}")

print("\n✓ Indexes created successfully!")
print("Note: Subsequent queries will be faster with these indexes.")
# Create indexes for better query performance
print("Creating database indexes...")
index_results = zpm.create_indexes()

print("\nIndex Status:")
print("=" * 60)
for index_name, status in index_results.items():
    print(f"{index_name:30s}: {status}")

print("\n✓ Indexes created successfully!")
print("Note: Subsequent queries will be faster with these indexes.")

Creating database indexes...

Index Status:
============================================================
idx_query                     : exists
idx_type                      : exists
idx_query_id_results          : exists
idx_inchi_id_results          : exists
idx_inchi                     : exists
idx_inchikey                  : exists
idx_inventory_query           : exists
idx_inventory_id              : exists

✓ Indexes created successfully!
Note: Subsequent queries will be faster with these indexes.

13. Creating Custom Views¶

Create database views for frequently used queries.

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# Create a view for CAS to InChI mapping
view_name = "cas_to_inchi_view"
sql_query = """
    SELECT aq.query AS cas, s.inchi, s.inchikey
    FROM api_ready_query aq
    JOIN api_results ar ON aq.query_id = ar.query_id
    JOIN substances s ON ar.inchi_id = s.inchi_id
    WHERE aq.type = 'CAS Registry Number' AND ar.rank = 1
"""

success = zpm.create_view(view_name, sql_query)

if success:
    print(f"✓ View '{view_name}' created successfully!")
    
    # Query the view
    zpm.cursor.execute(f"SELECT * FROM {view_name} LIMIT 5")
    print(f"\nSample data from view:")
    print("=" * 60)
    for row in zpm.cursor.fetchall():
        print(f"CAS: {row[0]}, InChIKey: {row[2][:27]}...")
    
    # Clean up - drop the view
    zpm.cursor.execute(f"DROP VIEW IF EXISTS {view_name}")
    zpm.conn.commit()
    print(f"\n✓ View dropped for cleanup")
else:
    print(f"✗ Failed to create view")
# Create a view for CAS to InChI mapping
view_name = "cas_to_inchi_view"
sql_query = """
    SELECT aq.query AS cas, s.inchi, s.inchikey
    FROM api_ready_query aq
    JOIN api_results ar ON aq.query_id = ar.query_id
    JOIN substances s ON ar.inchi_id = s.inchi_id
    WHERE aq.type = 'CAS Registry Number' AND ar.rank = 1
"""

success = zpm.create_view(view_name, sql_query)

if success:
    print(f"✓ View '{view_name}' created successfully!")
    
    # Query the view
    zpm.cursor.execute(f"SELECT * FROM {view_name} LIMIT 5")
    print(f"\nSample data from view:")
    print("=" * 60)
    for row in zpm.cursor.fetchall():
        print(f"CAS: {row[0]}, InChIKey: {row[2][:27]}...")
    
    # Clean up - drop the view
    zpm.cursor.execute(f"DROP VIEW IF EXISTS {view_name}")
    zpm.conn.commit()
    print(f"\n✓ View dropped for cleanup")
else:
    print(f"✗ Failed to create view")

✓ View 'cas_to_inchi_view' created successfully!

Sample data from view:
============================================================
CAS: 121-20-0, InChIKey: SHCRDCOTRILILT-WOBDGSLYSA-N...
CAS: 121-20-0, InChIKey: SHCRDCOTRILILT-WOBDGSLYSA-N...
CAS: 121-20-0, InChIKey: SHCRDCOTRILILT-WOBDGSLYSA-N...
CAS: 121-20-0, InChIKey: SHCRDCOTRILILT-WOBDGSLYSA-N...
CAS: 25646-71-3, InChIKey: NPDFXFLCEDDWEG-UHFFFAOYSA-N...

✓ View dropped for cleanup

14. Complete Example: Workflow for Multiple Chemicals¶

Here's a complete workflow demonstrating how to process multiple chemicals efficiently.

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import pandas as pd

# Get a sample of CAS numbers to process
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'CAS Registry Number' 
    LIMIT 10
""")
cas_numbers = [row[0] for row in zpm.cursor.fetchall()]

print(f"Processing {len(cas_numbers)} chemicals...")
print("=" * 80)

# Batch get all the data we need
query_ids = zpm.batch_query_cas(cas_numbers)
smiles_data = zpm.batch_get_smiles_from_cas(cas_numbers)
names_data = zpm.batch_get_names(cas_numbers)

# Create a pandas DataFrame
data = []
for cas in cas_numbers:
    data.append({
        'CAS': cas,
        'Query_ID': query_ids.get(cas),
        'SMILES': smiles_data.get(cas),
        'Names_Count': len(names_data.get(cas, [])),
        'First_Name': names_data.get(cas, [''])[0] if names_data.get(cas) else ''
    })

df = pd.DataFrame(data)

print("\nResults Summary:")
print(df.to_string(index=False, max_colwidth=50))

print(f"\n✓ Processed {len(cas_numbers)} chemicals successfully!")
print(f"  - {df['SMILES'].notna().sum()} have SMILES")
print(f"  - {df[df['Names_Count'] > 0].shape[0]} have alternative names")
import pandas as pd

# Get a sample of CAS numbers to process
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'CAS Registry Number' 
    LIMIT 10
""")
cas_numbers = [row[0] for row in zpm.cursor.fetchall()]

print(f"Processing {len(cas_numbers)} chemicals...")
print("=" * 80)

# Batch get all the data we need
query_ids = zpm.batch_query_cas(cas_numbers)
smiles_data = zpm.batch_get_smiles_from_cas(cas_numbers)
names_data = zpm.batch_get_names(cas_numbers)

# Create a pandas DataFrame
data = []
for cas in cas_numbers:
    data.append({
        'CAS': cas,
        'Query_ID': query_ids.get(cas),
        'SMILES': smiles_data.get(cas),
        'Names_Count': len(names_data.get(cas, [])),
        'First_Name': names_data.get(cas, [''])[0] if names_data.get(cas) else ''
    })

df = pd.DataFrame(data)

print("\nResults Summary:")
print(df.to_string(index=False, max_colwidth=50))

print(f"\n✓ Processed {len(cas_numbers)} chemicals successfully!")
print(f"  - {df['SMILES'].notna().sum()} have SMILES")
print(f"  - {df[df['Names_Count'] > 0].shape[0]} have alternative names")

Processing 10 chemicals...
================================================================================

Results Summary:
        CAS  Query_ID                                             SMILES  Names_Count                                         First_Name
   121-20-0         1 C/C=C\CC1=C(C)[C@@H](OC(=O)[C@@H]2[C@@H](/C=C(\...            7 Cyclopropanecarboxylic acid, 3-(3-methoxy-2-met...
 25646-71-3         4 CCN(CCNS(C)(=O)=O)c1ccc(N)c(C)c1.CCN(CCNS(C)(=O...           10                                              (2:3)
 76823-93-3         9                            N#CCCSCc1csc(NC(=N)N)n1            3 3-(2-(diaminomethyleneamino)thiazol-4-ylmethylt...
177964-68-0        11      COCc1c(C(C)C)nc(C(C)C)c(C=CC=O)c1-c1ccc(F)cc1            4                          (E)-3-(4-(4-(E)-2-butenal
 27955-94-8        14            CC(c1ccc(O)cc1)(c1ccc(O)cc1)c1ccc(O)cc1            7           4,4',4''-(etaani-1,1,1-triyyli)trifenoli
122886-55-9        16 CCCCCCCCN=C(O)Nc1ccc(Cc2ccc(NC(O)=NCCCCCCCC)cc2...            8 Urea, N-octyl-N'-[4-[[4-[[(octylamino)carbonyl]...
   111-40-0        18                                            NCCNCCN           19              1,2-Ethanediamine, N1-(2-aminoethyl)-
151798-26-4        25 Cc1cccc(N=C(O)c2cc3ccccc3c(N=Nc3ccc4c(c3)C(=O)c...            2 2-[2-hydroksi-3-(2-kloorifenyyli)karbamoyyli-1-...
    96-29-7        27                                          CCC(C)=NO           18                                     butanone oxime
 16672-87-0        32                                      O=P(O)(O)CCCl            8                                      Chlorethephon

✓ Processed 10 chemicals successfully!
  - 10 have SMILES
  - 10 have alternative names

15. Summary and Best Practices¶

Key Features:¶

Simple Queries: query_cas(), query_name()
Fuzzy Matching: query_similar_name() with configurable score cutoff
Identifier Conversion: Convert between CAS, InChI, InChIKey, and SMILES
Batch Operations: Process multiple chemicals efficiently
Advanced Search: Regex patterns and substructure matching
Export: Save results to CSV for further analysis
Performance: Create indexes for faster queries

Best Practices:¶

Use batch methods when processing multiple chemicals
Create indexes before running many queries
Use fuzzy matching for user input with potential typos
Set appropriate score cutoffs for fuzzy matching (70-90 is typical)
Export results to CSV for sharing or further analysis

Performance Tips:¶

Batch operations are much faster than individual queries
Create indexes once at the start if doing many queries
Use score_cutoff parameter to limit fuzzy search results
Limit substructure searches with max_results parameter

14. New ID Table Methods - Get Complete Identifier Tables¶

The ZeroPM class now provides four new methods to retrieve complete identifier tables from different starting points: InChI, InChIKey, and chemical names. These complement the existing get_id_table_from_cas() method.

14.1 Get ID Table from InChI¶

The get_id_table_from_inchi() method returns all identifiers for a given InChI string.

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# First, let's get an InChI from a known CAS to use as an example
formaldehyde_table = zpm.get_id_table_from_cas("50-00-0")
if formaldehyde_table is not None and len(formaldehyde_table) > 0:
    example_inchi = formaldehyde_table['inchi'].iloc[0]
    print(f"Example InChI: {example_inchi}\n")
    
    # Now get the ID table from InChI
    df_from_inchi = zpm.get_id_table_from_inchi(example_inchi)
    print("ID Table from InChI:")
    print(df_from_inchi)
    print(f"\nColumns: {list(df_from_inchi.columns)}")
else:
    print("Could not find formaldehyde in database")
# First, let's get an InChI from a known CAS to use as an example
formaldehyde_table = zpm.get_id_table_from_cas("50-00-0")
if formaldehyde_table is not None and len(formaldehyde_table) > 0:
    example_inchi = formaldehyde_table['inchi'].iloc[0]
    print(f"Example InChI: {example_inchi}\n")
    
    # Now get the ID table from InChI
    df_from_inchi = zpm.get_id_table_from_inchi(example_inchi)
    print("ID Table from InChI:")
    print(df_from_inchi)
    print(f"\nColumns: {list(df_from_inchi.columns)}")
else:
    print("Could not find formaldehyde in database")

Example InChI: InChI=1S/CH2O/c1-2/h1H2

ID Table from InChI:
                      inchi                     inchikey  inchi_id  query_id  \
0   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227      8671   
1   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227      8672   
2   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227     35726   
3   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227     35725   
4   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    310465   
5   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    367895   
6   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    325078   
7   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    325578   
8   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    361811   
9   InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227     18623   
10  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227      1759   
11  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227      7762   
12  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227      1760   
13  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    395145   
14  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    124573   
15  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    365734   
16  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227     91354   
17  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    361811   
18  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    310465   
19  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227     36880   
20  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    124574   
21  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227     11512   
22  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    366233   
23  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    325879   
24  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    325877   
25  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227     11565   
26  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227    367017   
27  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     32227      5472   

    rank         cas                                            sources  \
0      1     50-00-0  Inventory of Chemicals; Chemical Data Reportin...   
1      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
2      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
3      1  30525-89-4  Inventory of Chemicals; Chemical Data Reportin...   
4      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
5      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
6      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
7      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
8      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
9      2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
10     2    630-08-0  Inventory of Chemicals; Chemical Data Reportin...   
11     2    108-62-3  Inventory of Chemicals; Chemical Data Reportin...   
12     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
13     2  63101-50-8  Inventory of Chemicals; Chemical Data Reportin...   
14     2   1664-98-8  Inventory of Chemicals; Chemical Data Reportin...   
15     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
16     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
17     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
18     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
19     3         NaN  Inventory of Chemicals; Chemical Data Reportin...   
20     6         NaN  Inventory of Chemicals; Chemical Data Reportin...   
21     7         NaN  Inventory of Chemicals; Chemical Data Reportin...   
22     7         NaN  Inventory of Chemicals; Chemical Data Reportin...   
23     7         NaN  Inventory of Chemicals; Chemical Data Reportin...   
24     7         NaN  Inventory of Chemicals; Chemical Data Reportin...   
25    11         NaN  Inventory of Chemicals; Chemical Data Reportin...   
26    11         NaN  Inventory of Chemicals; Chemical Data Reportin...   
27    11         NaN  Inventory of Chemicals; Chemical Data Reportin...   

                                             synonyms  
0   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
1   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
2   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
3   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
4   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
5   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
6   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
7   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
8   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
9   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
10  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
11  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
12  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
13  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
14  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
15  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
16  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
17  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
18  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
19  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
20  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
21  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
22  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
23  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
24  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
25  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
26  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
27  formaldehyde ...%; FORMALIN; Formaldehyde; for...  

Columns: ['inchi', 'inchikey', 'inchi_id', 'query_id', 'rank', 'cas', 'sources', 'synonyms']

14.2 Batch Get ID Tables from InChI List¶

Process multiple InChI strings at once:

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# Get multiple InChIs from the database to use as examples
example_cas_list = ["50-00-0", "50-78-2", "64-17-5"]  # formaldehyde, aspirin, ethanol
inchi_list = []

for cas in example_cas_list:
    table = zpm.get_id_table_from_cas(cas)
    if table is not None and len(table) > 0:
        inchi_list.append(table['inchi'].iloc[0])

print(f"Testing with {len(inchi_list)} InChI strings\n")

# Batch process
batch_df = zpm.batch_get_id_table_from_inchi(inchi_list)
print("Batch ID Table from InChI list:")
print(batch_df[['inchi', 'inchikey', 'cas', 'rank']].head(10))
print(f"\nTotal rows: {len(batch_df)}")
print(f"Unique InChIs processed: {batch_df['inchi'].nunique()}")
# Get multiple InChIs from the database to use as examples
example_cas_list = ["50-00-0", "50-78-2", "64-17-5"]  # formaldehyde, aspirin, ethanol
inchi_list = []

for cas in example_cas_list:
    table = zpm.get_id_table_from_cas(cas)
    if table is not None and len(table) > 0:
        inchi_list.append(table['inchi'].iloc[0])

print(f"Testing with {len(inchi_list)} InChI strings\n")

# Batch process
batch_df = zpm.batch_get_id_table_from_inchi(inchi_list)
print("Batch ID Table from InChI list:")
print(batch_df[['inchi', 'inchikey', 'cas', 'rank']].head(10))
print(f"\nTotal rows: {len(batch_df)}")
print(f"Unique InChIs processed: {batch_df['inchi'].nunique()}")

Testing with 3 InChI strings

Batch ID Table from InChI list:
                     inchi                     inchikey         cas  rank
0  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N     50-00-0     1
1  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
2  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
3  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N  30525-89-4     1
4  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
5  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
6  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
7  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
8  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
9  InChI=1S/CH2O/c1-2/h1H2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     2

Total rows: 86
Unique InChIs processed: 3

14.3 Get ID Table from InChIKey¶

The get_id_table_from_inchikey() method returns all identifiers for a given InChIKey string.

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# Get an InChIKey example from formaldehyde
if formaldehyde_table is not None and len(formaldehyde_table) > 0:
    example_inchikey = formaldehyde_table['inchikey'].iloc[0]
    print(f"Example InChIKey: {example_inchikey}\n")
    
    # Now get the ID table from InChIKey
    df_from_inchikey = zpm.get_id_table_from_inchikey(example_inchikey)
    print("ID Table from InChIKey:")
    print(df_from_inchikey)
    print(f"\nColumns: {list(df_from_inchikey.columns)}")
else:
    print("Could not find formaldehyde in database")
# Get an InChIKey example from formaldehyde
if formaldehyde_table is not None and len(formaldehyde_table) > 0:
    example_inchikey = formaldehyde_table['inchikey'].iloc[0]
    print(f"Example InChIKey: {example_inchikey}\n")
    
    # Now get the ID table from InChIKey
    df_from_inchikey = zpm.get_id_table_from_inchikey(example_inchikey)
    print("ID Table from InChIKey:")
    print(df_from_inchikey)
    print(f"\nColumns: {list(df_from_inchikey.columns)}")
else:
    print("Could not find formaldehyde in database")

Example InChIKey: WSFSSNUMVMOOMR-UHFFFAOYSA-N

ID Table from InChIKey:
                       inchikey                    inchi  inchi_id  query_id  \
0   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227      8671   
1   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227      8672   
2   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227     35726   
3   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227     35725   
4   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    310465   
5   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    367895   
6   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    325078   
7   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    325578   
8   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    361811   
9   WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227     18623   
10  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227      1759   
11  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227      7762   
12  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227      1760   
13  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    395145   
14  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    124573   
15  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    365734   
16  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227     91354   
17  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    361811   
18  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    310465   
19  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227     36880   
20  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    124574   
21  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227     11512   
22  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    366233   
23  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    325879   
24  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    325877   
25  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227     11565   
26  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227    367017   
27  WSFSSNUMVMOOMR-UHFFFAOYSA-N  InChI=1S/CH2O/c1-2/h1H2     32227      5472   

    rank         cas                                            sources  \
0      1     50-00-0  Inventory of Chemicals; Chemical Data Reportin...   
1      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
2      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
3      1  30525-89-4  Inventory of Chemicals; Chemical Data Reportin...   
4      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
5      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
6      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
7      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
8      1         NaN  Inventory of Chemicals; Chemical Data Reportin...   
9      2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
10     2    630-08-0  Inventory of Chemicals; Chemical Data Reportin...   
11     2    108-62-3  Inventory of Chemicals; Chemical Data Reportin...   
12     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
13     2  63101-50-8  Inventory of Chemicals; Chemical Data Reportin...   
14     2   1664-98-8  Inventory of Chemicals; Chemical Data Reportin...   
15     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
16     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
17     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
18     2         NaN  Inventory of Chemicals; Chemical Data Reportin...   
19     3         NaN  Inventory of Chemicals; Chemical Data Reportin...   
20     6         NaN  Inventory of Chemicals; Chemical Data Reportin...   
21     7         NaN  Inventory of Chemicals; Chemical Data Reportin...   
22     7         NaN  Inventory of Chemicals; Chemical Data Reportin...   
23     7         NaN  Inventory of Chemicals; Chemical Data Reportin...   
24     7         NaN  Inventory of Chemicals; Chemical Data Reportin...   
25    11         NaN  Inventory of Chemicals; Chemical Data Reportin...   
26    11         NaN  Inventory of Chemicals; Chemical Data Reportin...   
27    11         NaN  Inventory of Chemicals; Chemical Data Reportin...   

                                             synonyms  
0   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
1   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
2   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
3   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
4   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
5   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
6   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
7   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
8   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
9   formaldehyde ...%; FORMALIN; Formaldehyde; for...  
10  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
11  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
12  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
13  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
14  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
15  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
16  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
17  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
18  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
19  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
20  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
21  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
22  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
23  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
24  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
25  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
26  formaldehyde ...%; FORMALIN; Formaldehyde; for...  
27  formaldehyde ...%; FORMALIN; Formaldehyde; for...  

Columns: ['inchikey', 'inchi', 'inchi_id', 'query_id', 'rank', 'cas', 'sources', 'synonyms']

14.4 Batch Get ID Tables from InChIKey List¶

Process multiple InChIKey strings at once:

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# Get InChIKeys from our example CAS list
inchikey_list = []
for cas in example_cas_list:
    table = zpm.get_id_table_from_cas(cas)
    if table is not None and len(table) > 0:
        inchikey_list.append(table['inchikey'].iloc[0])

print(f"Testing with {len(inchikey_list)} InChIKey strings:")
for key in inchikey_list:
    print(f"  - {key}")

# Batch process
batch_df_keys = zpm.batch_get_id_table_from_inchikey(inchikey_list)
print("\nBatch ID Table from InChIKey list:")
print(batch_df_keys[['inchikey', 'cas', 'rank']].head(10))
print(f"\nTotal rows: {len(batch_df_keys)}")
print(f"Unique InChIKeys processed: {batch_df_keys['inchikey'].nunique()}")
# Get InChIKeys from our example CAS list
inchikey_list = []
for cas in example_cas_list:
    table = zpm.get_id_table_from_cas(cas)
    if table is not None and len(table) > 0:
        inchikey_list.append(table['inchikey'].iloc[0])

print(f"Testing with {len(inchikey_list)} InChIKey strings:")
for key in inchikey_list:
    print(f"  - {key}")

# Batch process
batch_df_keys = zpm.batch_get_id_table_from_inchikey(inchikey_list)
print("\nBatch ID Table from InChIKey list:")
print(batch_df_keys[['inchikey', 'cas', 'rank']].head(10))
print(f"\nTotal rows: {len(batch_df_keys)}")
print(f"Unique InChIKeys processed: {batch_df_keys['inchikey'].nunique()}")

Testing with 3 InChIKey strings:
  - WSFSSNUMVMOOMR-UHFFFAOYSA-N
  - BSYNRYMUTXBXSQ-UHFFFAOYSA-N
  - LFQSCWFLJHTTHZ-UHFFFAOYSA-N

Batch ID Table from InChIKey list:
                      inchikey         cas  rank
0  WSFSSNUMVMOOMR-UHFFFAOYSA-N     50-00-0     1
1  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
2  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
3  WSFSSNUMVMOOMR-UHFFFAOYSA-N  30525-89-4     1
4  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
5  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
6  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
7  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
8  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     1
9  WSFSSNUMVMOOMR-UHFFFAOYSA-N         NaN     2

Total rows: 86
Unique InChIKeys processed: 3

14.5 Get ID Table from Chemical Name¶

The get_id_table_from_name() method returns all identifiers for a given chemical name (exact match).

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# Try to find a chemical name in the database
# First, let's see what names are available for formaldehyde
formaldehyde_names = zpm.get_names("50-00-0")
print(f"Available names for formaldehyde (CAS 50-00-0): {formaldehyde_names[:5]}\n")

if formaldehyde_names:
    # Use the first name
    example_name = formaldehyde_names[0]
    print(f"Using name: '{example_name}'\n")
    
    # Get ID table from name
    df_from_name = zpm.get_id_table_from_name(example_name)
    if df_from_name is not None:
        print("ID Table from Name:")
        print(df_from_name)
        print(f"\nColumns: {list(df_from_name.columns)}")
    else:
        print(f"Name '{example_name}' not found in database as a query")
else:
    print("No names found for formaldehyde")
# Try to find a chemical name in the database
# First, let's see what names are available for formaldehyde
formaldehyde_names = zpm.get_names("50-00-0")
print(f"Available names for formaldehyde (CAS 50-00-0): {formaldehyde_names[:5]}\n")

if formaldehyde_names:
    # Use the first name
    example_name = formaldehyde_names[0]
    print(f"Using name: '{example_name}'\n")
    
    # Get ID table from name
    df_from_name = zpm.get_id_table_from_name(example_name)
    if df_from_name is not None:
        print("ID Table from Name:")
        print(df_from_name)
        print(f"\nColumns: {list(df_from_name.columns)}")
    else:
        print(f"Name '{example_name}' not found in database as a query")
else:
    print("No names found for formaldehyde")

Available names for formaldehyde (CAS 50-00-0): ['formaldehyde ...%', 'FORMALIN', 'Formaldehyde', 'formaldehyde ... %', 'Formalin']

Using name: 'formaldehyde ...%'

ID Table from Name:
                name  query_id inchi_id  rank inchi inchikey   cas  \
0  formaldehyde ...%    104113     None  None  None     None  None   

        sources  
0  CLP Annex VI  

Columns: ['name', 'query_id', 'inchi_id', 'rank', 'inchi', 'inchikey', 'cas', 'sources']

14.6 Batch Get ID Tables from Name List¶

Process multiple chemical names at once:

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# Get a few chemical names from the database
# Query some names directly from the database
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'chemical name'
    LIMIT 5
""")
name_results = zpm.cursor.fetchall()
name_list = [row[0] for row in name_results]

print(f"Testing with {len(name_list)} chemical names:")
for name in name_list:
    print(f"  - {name}")

# Batch process
batch_df_names = zpm.batch_get_id_table_from_name(name_list)
if batch_df_names is not None and len(batch_df_names) > 0:
    print("\nBatch ID Table from Name list:")
    print(batch_df_names[['name', 'cas', 'inchikey', 'rank']].head(10))
    print(f"\nTotal rows: {len(batch_df_names)}")
    print(f"Unique names processed: {batch_df_names['name'].nunique()}")
else:
    print("\nNo results found for the name list")
# Get a few chemical names from the database
# Query some names directly from the database
zpm.cursor.execute("""
    SELECT query 
    FROM api_ready_query 
    WHERE type = 'chemical name'
    LIMIT 5
""")
name_results = zpm.cursor.fetchall()
name_list = [row[0] for row in name_results]

print(f"Testing with {len(name_list)} chemical names:")
for name in name_list:
    print(f"  - {name}")

# Batch process
batch_df_names = zpm.batch_get_id_table_from_name(name_list)
if batch_df_names is not None and len(batch_df_names) > 0:
    print("\nBatch ID Table from Name list:")
    print(batch_df_names[['name', 'cas', 'inchikey', 'rank']].head(10))
    print(f"\nTotal rows: {len(batch_df_names)}")
    print(f"Unique names processed: {batch_df_names['name'].nunique()}")
else:
    print("\nNo results found for the name list")

Testing with 5 chemical names:
  - cinerin II
  - 3-(but-2-enyl)-2-methyl-4-oxocyclopent-2-enyl 2,2-dimethyl-3-(3-methoxy-2-methyl-3-oxoprop-1-enyl)cyclopropanecarboxylate
  - N-(2-(4-amino-N-ethyl-m-toluidino)ethyl)methanesulphonamide sesquisulphate
  - 4-(N-ethyl-N-2-methanesulphonylaminoethyl)-2-methylphenylenediamine sesquisulphate monohydrate
  - N-(2-(4-amino-N-ethyl-m-toluidino)ethyl)methanesulfonamide sesquisulfate

Batch ID Table from Name list:
         name       cas                     inchikey rank
0  cinerin II  121-20-0  SHCRDCOTRILILT-WOBDGSLYSA-N    1
1  cinerin II  121-20-0  SHCRDCOTRILILT-DCXZXJRMSA-N    2
2  cinerin II  121-54-0  SHCRDCOTRILILT-DCXZXJRMSA-N    2
3  cinerin II  121-20-0  SHCRDCOTRILILT-UHFFFAOYSA-N    3
4  cinerin II       NaN  SHCRDCOTRILILT-LGPFIRNVSA-N    4
5  cinerin II       NaN  SHCRDCOTRILILT-YZDDFNNUSA-N    5
6  cinerin II       NaN  SHCRDCOTRILILT-SIMJFJABSA-N    6
7  cinerin II       NaN  SHCRDCOTRILILT-GFCFHAQJSA-N    7
8  cinerin II       NaN  WZRUHNUBXOTVHG-KTVYOGNXSA-N    8
9  cinerin II       NaN  KHNZEDVIMMWGSA-DCXZXJRMSA-N    9

Total rows: 19
Unique names processed: 5

14.7 Summary of ID Table Methods¶

All six ID table methods provide:

Complete identifier mapping: Returns all related identifiers in a DataFrame
Rank information: Shows the relevance/confidence of each match
Batch processing: Efficient handling of multiple queries
Consistent output format: Easy to combine and analyze results

Use cases:

get_id_table_from_cas() - When you have CAS numbers and need all associated identifiers
get_id_table_from_inchi() - When you have InChI strings from calculations or other sources
get_id_table_from_inchikey() - When you have InChIKeys from databases or publications
get_id_table_from_name() - When you have exact chemical names (not fuzzy matching)

The batch versions are recommended when processing multiple chemicals for better performance.

15. CAS Conversion Methods - Convert to CAS from Various Identifiers¶

The ZeroPM class provides methods to convert from different types of chemical identifiers to CAS numbers.

15.1 Get CAS from Chemical Name¶

Convert exact chemical names to CAS numbers:

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# Example: Get CAS from a chemical name
# Use a name from our previous examples
if formaldehyde_names:
    test_name = formaldehyde_names[0]  # "Formalin"
    print(f"Chemical name: {test_name}")
    
    cas_from_name = zpm.get_cas_from_name(test_name)
    print(f"CAS number(s): {cas_from_name}")
    print(f"Type: {type(cas_from_name)}")
    
    # If multiple CAS numbers are returned
    if isinstance(cas_from_name, list):
        print(f"\nFound {len(cas_from_name)} CAS numbers for this name")
        for i, cas in enumerate(cas_from_name, 1):
            print(f"  {i}. {cas}")
else:
    # Try with another name
    cas_from_name = zpm.get_cas_from_name("methanol")
    print(f"Methanol CAS: {cas_from_name}")
# Example: Get CAS from a chemical name
# Use a name from our previous examples
if formaldehyde_names:
    test_name = formaldehyde_names[0]  # "Formalin"
    print(f"Chemical name: {test_name}")
    
    cas_from_name = zpm.get_cas_from_name(test_name)
    print(f"CAS number(s): {cas_from_name}")
    print(f"Type: {type(cas_from_name)}")
    
    # If multiple CAS numbers are returned
    if isinstance(cas_from_name, list):
        print(f"\nFound {len(cas_from_name)} CAS numbers for this name")
        for i, cas in enumerate(cas_from_name, 1):
            print(f"  {i}. {cas}")
else:
    # Try with another name
    cas_from_name = zpm.get_cas_from_name("methanol")
    print(f"Methanol CAS: {cas_from_name}")

Chemical name: formaldehyde ...%
CAS number(s): None
Type: <class 'NoneType'>

15.2 Get CAS from SMILES¶

Convert SMILES strings to CAS numbers (already existed, shown for completeness):

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# Example: Get CAS from SMILES
smiles_examples = {
    "C": "Methane",
    "CO": "Methanol", 
    "CCO": "Ethanol",
    "C=O": "Formaldehyde"
}

print("Converting SMILES to CAS:")
for smiles, name in smiles_examples.items():
    cas = zpm.get_cas_from_smiles(smiles)
    print(f"  {smiles:6s} ({name:15s}): {cas}")
# Example: Get CAS from SMILES
smiles_examples = {
    "C": "Methane",
    "CO": "Methanol", 
    "CCO": "Ethanol",
    "C=O": "Formaldehyde"
}

print("Converting SMILES to CAS:")
for smiles, name in smiles_examples.items():
    cas = zpm.get_cas_from_smiles(smiles)
    print(f"  {smiles:6s} ({name:15s}): {cas}")

Converting SMILES to CAS:
  C      (Methane        ): ['8006-14-2', '74-82-8', '1333-86-4', '7440-44-0', '7782-42-5', '308068-56-6', '76-49-3', '133-11-9', '16291-96-6', '676-80-2', '125612-26-2', '115383-22-7', '7782-40-3', '1034343-98-0', '99685-96-8', '6532-48-5', '14762-74-4', '131159-39-2', '90597-58-3', '3109-63-5', '64365-11-3']
  CO     (Methanol       ): ['67-56-1', '3473-63-0', '125-04-2', '1849-29-2', '90-05-1', '276863-95-7', '533-67-5', '97-67-6', '10399-13-0', '7682-20-4', '2969-81-5', '122-66-7', '135646-98-9', '526-73-8', '5026-62-0', '72-18-4', '147-85-3', '73231-34-2', '14166-21-3', '80875-98-5', '288-32-4', '53-03-2', '59-51-8', '62211-93-2', '76721-89-6', '526-83-0', '15307-79-6', '2016-36-6', '119-56-2', '520-26-3', '63675-74-1', '2746-19-2', '2295-31-0', '61278-21-5', '38966-21-1', '93-60-7', '41340-36-7', '507-09-5', '101020-79-5', '14742-26-8', '24198-97-8', '1116-77-4', '63-91-2', '221176-49-4', '26473-47-2', '27918-19-0', '42288-26-6', '73851-70-4', '73-40-5', '120-94-5', '183288-43-9', '112827-99-3', '18297-63-7', '28049-61-8', '122111-11-9', '6368-20-3', '123-30-8', '141645-16-1', '1264-62-6', '481-29-8', '1455-13-6', '104987-12-4', '86393-33-1', '110567-22-1', '5202-89-1', '192725-50-1', '40431-63-8', '134-85-0', '179463-17-3', '219861-08-2', '56-12-2', '51146-56-6', '149437-76-3', '256-96-2', '5543-57-7', '72126-78-4', '143322-58-1', '183193-59-1', '109-11-5', '41078-70-0', '138530-94-6', '24169-02-6', '125-65-5', '811-98-3', '27262-45-9', '147-71-7', '84680-54-6', '51146-57-7', '61036-62-2', '27262-47-1', '162515-68-6', '114772-34-8', '720-94-5', '50-03-3', '112704-79-7', '93957-50-7', '5445-51-2', '1075-89-4', '473-98-3', '25322-68-3', '102767-31-7', '636-21-5', '13408-09-8', '58-00-4', '78441-62-0', '131-01-1', '3788-94-1', '42835-25-6', '80841-78-7', '46022-05-3', '43229-70-5', '382-67-2', '86604-78-6', '183288-44-0', '5543-58-8', '113082-99-8', '71749-03-6', '18725-37-6', '4441-63-8', '324763-51-1', '85933-19-3', '35271-74-0', '16673-34-0', '465-99-6', '976-71-6', '117976-90-6', '139481-59-7']
  CCO    (Ethanol        ): ['64-17-5', '68475-56-9', '68476-78-8', '1516-08-1', '14742-23-5', '925-93-9', '9002-89-5', '90604-31-2']
  C=O    (Formaldehyde   ): ['50-00-0', '30525-89-4', '630-08-0', '108-62-3', '63101-50-8', '1664-98-8']

15.3 Get CAS from Molecular Formula¶

Convert molecular formulas to CAS numbers. Note that formulas are not unique - many isomers can share the same formula:

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# Example: Get CAS from molecular formula
# Warning: This can be slow for the first run as it scans the entire database
formulas_to_test = ["CH2O", "H2O", "CH4O"]

print("Converting molecular formulas to CAS numbers:\n")
for formula in formulas_to_test:
    print(f"Formula: {formula}")
    cas_list = zpm.get_cas_from_formula(formula)
    
    if cas_list:
        print(f"  Found {len(cas_list)} chemicals with this formula")
        # Show first 5 CAS numbers
        for i, cas in enumerate(cas_list[:5], 1):
            print(f"    {i}. {cas}")
        if len(cas_list) > 5:
            print(f"    ... and {len(cas_list) - 5} more")
    else:
        print(f"  No chemicals found with formula {formula}")
    print()
# Example: Get CAS from molecular formula
# Warning: This can be slow for the first run as it scans the entire database
formulas_to_test = ["CH2O", "H2O", "CH4O"]

print("Converting molecular formulas to CAS numbers:\n")
for formula in formulas_to_test:
    print(f"Formula: {formula}")
    cas_list = zpm.get_cas_from_formula(formula)
    
    if cas_list:
        print(f"  Found {len(cas_list)} chemicals with this formula")
        # Show first 5 CAS numbers
        for i, cas in enumerate(cas_list[:5], 1):
            print(f"    {i}. {cas}")
        if len(cas_list) > 5:
            print(f"    ... and {len(cas_list) - 5} more")
    else:
        print(f"  No chemicals found with formula {formula}")
    print()

Converting molecular formulas to CAS numbers:

Formula: CH2O

  Found 7 chemicals with this formula
    1. 108-62-3
    2. 1664-98-8
    3. 30525-89-4
    4. 3228-27-1
    5. 50-00-0
    ... and 2 more

Formula: H2O

  Found 6 chemicals with this formula
    1. 13768-40-6
    2. 14280-30-9
    3. 14314-42-2
    4. 17778-80-2
    5. 7732-18-5
    ... and 1 more

Formula: CH4O

  Found 129 chemicals with this formula
    1. 101020-79-5
    2. 102767-31-7
    3. 10399-13-0
    4. 104987-12-4
    5. 1075-89-4
    ... and 124 more

15.4 Batch CAS Conversion Methods¶

Process multiple identifiers efficiently:

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# Batch convert SMILES to CAS
smiles_list = ["C", "CO", "CCO", "C=O"]
print("Batch SMILES to CAS conversion:")
results_smiles = zpm.batch_get_cas_from_smiles(smiles_list)
for smiles, cas in results_smiles.items():
    print(f"  {smiles:6s} -> {cas}")

print("\n" + "="*50 + "\n")

# Batch convert names to CAS
if formaldehyde_names:
    # Use a few names from formaldehyde
    test_names = formaldehyde_names[:3]
    print(f"Batch names to CAS conversion:")
    results_names = zpm.batch_get_cas_from_name(test_names)
    for name, cas in results_names.items():
        print(f"  {name:20s} -> {cas}")

print("\n" + "="*50 + "\n")

# Batch convert formulas to CAS
formula_batch = ["CH4", "CH4O", "C2H6O"]
print("Batch formulas to CAS conversion:")
results_formulas = zpm.batch_get_cas_from_formula(formula_batch)
for formula, cas_list in results_formulas.items():
    if cas_list:
        print(f"  {formula:8s} -> {len(cas_list)} chemicals found")
    else:
        print(f"  {formula:8s} -> Not found")
# Batch convert SMILES to CAS
smiles_list = ["C", "CO", "CCO", "C=O"]
print("Batch SMILES to CAS conversion:")
results_smiles = zpm.batch_get_cas_from_smiles(smiles_list)
for smiles, cas in results_smiles.items():
    print(f"  {smiles:6s} -> {cas}")

print("\n" + "="*50 + "\n")

# Batch convert names to CAS
if formaldehyde_names:
    # Use a few names from formaldehyde
    test_names = formaldehyde_names[:3]
    print(f"Batch names to CAS conversion:")
    results_names = zpm.batch_get_cas_from_name(test_names)
    for name, cas in results_names.items():
        print(f"  {name:20s} -> {cas}")

print("\n" + "="*50 + "\n")

# Batch convert formulas to CAS
formula_batch = ["CH4", "CH4O", "C2H6O"]
print("Batch formulas to CAS conversion:")
results_formulas = zpm.batch_get_cas_from_formula(formula_batch)
for formula, cas_list in results_formulas.items():
    if cas_list:
        print(f"  {formula:8s} -> {len(cas_list)} chemicals found")
    else:
        print(f"  {formula:8s} -> Not found")

Batch SMILES to CAS conversion:
  C      -> ['8006-14-2', '74-82-8', '1333-86-4', '7440-44-0', '7782-42-5', '308068-56-6', '76-49-3', '133-11-9', '16291-96-6', '676-80-2', '125612-26-2', '115383-22-7', '7782-40-3', '1034343-98-0', '99685-96-8', '6532-48-5', '14762-74-4', '131159-39-2', '90597-58-3', '3109-63-5', '64365-11-3']
  CO     -> ['67-56-1', '3473-63-0', '125-04-2', '1849-29-2', '90-05-1', '276863-95-7', '533-67-5', '97-67-6', '10399-13-0', '7682-20-4', '2969-81-5', '122-66-7', '135646-98-9', '526-73-8', '5026-62-0', '72-18-4', '147-85-3', '73231-34-2', '14166-21-3', '80875-98-5', '288-32-4', '53-03-2', '59-51-8', '62211-93-2', '76721-89-6', '526-83-0', '15307-79-6', '2016-36-6', '119-56-2', '520-26-3', '63675-74-1', '2746-19-2', '2295-31-0', '61278-21-5', '38966-21-1', '93-60-7', '41340-36-7', '507-09-5', '101020-79-5', '14742-26-8', '24198-97-8', '1116-77-4', '63-91-2', '221176-49-4', '26473-47-2', '27918-19-0', '42288-26-6', '73851-70-4', '73-40-5', '120-94-5', '183288-43-9', '112827-99-3', '18297-63-7', '28049-61-8', '122111-11-9', '6368-20-3', '123-30-8', '141645-16-1', '1264-62-6', '481-29-8', '1455-13-6', '104987-12-4', '86393-33-1', '110567-22-1', '5202-89-1', '192725-50-1', '40431-63-8', '134-85-0', '179463-17-3', '219861-08-2', '56-12-2', '51146-56-6', '149437-76-3', '256-96-2', '5543-57-7', '72126-78-4', '143322-58-1', '183193-59-1', '109-11-5', '41078-70-0', '138530-94-6', '24169-02-6', '125-65-5', '811-98-3', '27262-45-9', '147-71-7', '84680-54-6', '51146-57-7', '61036-62-2', '27262-47-1', '162515-68-6', '114772-34-8', '720-94-5', '50-03-3', '112704-79-7', '93957-50-7', '5445-51-2', '1075-89-4', '473-98-3', '25322-68-3', '102767-31-7', '636-21-5', '13408-09-8', '58-00-4', '78441-62-0', '131-01-1', '3788-94-1', '42835-25-6', '80841-78-7', '46022-05-3', '43229-70-5', '382-67-2', '86604-78-6', '183288-44-0', '5543-58-8', '113082-99-8', '71749-03-6', '18725-37-6', '4441-63-8', '324763-51-1', '85933-19-3', '35271-74-0', '16673-34-0', '465-99-6', '976-71-6', '117976-90-6', '139481-59-7']
  CCO    -> ['64-17-5', '68475-56-9', '68476-78-8', '1516-08-1', '14742-23-5', '925-93-9', '9002-89-5', '90604-31-2']
  C=O    -> ['50-00-0', '30525-89-4', '630-08-0', '108-62-3', '63101-50-8', '1664-98-8']

==================================================

Batch names to CAS conversion:
  formaldehyde ...%    -> None
  FORMALIN             -> ['108-62-3', '1664-98-8', '30525-89-4', '50-00-0', '630-08-0', '63101-50-8']
  Formaldehyde         -> ['1003-90-3', '1034343-98-0', '106349-49-9', '108-62-3', '115383-22-7', '125612-26-2', '131159-39-2', '133-11-9', '1333-86-4', '14762-74-4', '16291-96-6', '1664-98-8', '25301-02-4', '30525-89-4', '308068-56-6', '3109-63-5', '50-00-0', '630-08-0', '63101-50-8', '64365-11-3', '6532-48-5', '676-80-2', '74-82-8', '7440-44-0', '76-49-3', '7782-40-3', '7782-42-5', '8006-14-2', '90597-58-3', '99685-96-8', '99896-05-6']

==================================================

Batch formulas to CAS conversion:

  CH4      -> 25 chemicals found
  CH4O     -> 129 chemicals found
  C2H6O    -> 10 chemicals found

15.5 Summary of CAS Conversion Methods¶

Available methods:

get_cas_from_name(name) - Convert exact chemical name to CAS
get_cas_from_smiles(smiles) - Convert SMILES to CAS (via InChI)
get_cas_from_formula(formula) - Convert molecular formula to CAS (returns list, non-unique)
get_cas_from_inchi(inchi) - Convert InChI to CAS
get_cas_from_inchikey(inchikey) - Convert InChIKey to CAS

Batch versions:

batch_get_cas_from_name(name_list)
batch_get_cas_from_smiles(smiles_list)
batch_get_cas_from_formula(formula_list)
batch_get_cas_from_inchikey(inchikey_list)

Important notes:

Name conversion requires exact matches. Use query_similar_name() for fuzzy matching
Formula conversion is not unique - multiple chemicals can have the same formula
SMILES conversion works by first converting to InChI
All methods return None if no match is found
Some methods may return a list of CAS numbers if multiple matches exist

Conclusion¶

This notebook has demonstrated the main features of the ZeroPM class:

✓ Querying by CAS number and chemical name
✓ Fuzzy name matching for handling variations
✓ Converting between chemical identifiers
✓ Batch processing for efficiency
✓ Advanced searches (regex, substructure)
✓ Exporting results to CSV
✓ Performance optimization with indexes

The ZeroPM class provides a convenient Python interface to the ZeroPM database, making it easy to work with chemical identifiers in your research and applications.

For more information, see the PROVESID documentation.