Medchem API Reference

Comprehensive reference for all medchem modules and functions.

Module: medchem.rules

Class: RuleFilters

Filter molecules based on multiple medicinal chemistry rules.

Constructor:

python

RuleFilters(rule_list: List[str])

Parameters:

rule_list: List of rule names to apply. See available rules below.

Methods:

python

__call__(mols: List[Chem.Mol], n_jobs: int = 1, progress: bool = False) -> Dict

mols: List of RDKit molecule objects
n_jobs: Number of parallel jobs (-1 uses all cores)
progress: Show progress bar
Returns: Dictionary with results for each rule

Example:

python

rfilter = mc.rules.RuleFilters(rule_list=["rule_of_five", "rule_of_cns"])
results = rfilter(mols=mol_list, n_jobs=-1, progress=True)

Module: medchem.rules.basic_rules

Individual rule functions that can be applied to single molecules.

rule_of_five()

python

rule_of_five(mol: Union[str, Chem.Mol]) -> bool

Lipinski's Rule of Five for oral bioavailability.

Criteria:

Molecular weight ≤ 500 Da
LogP ≤ 5
H-bond donors ≤ 5
H-bond acceptors ≤ 10

Parameters:

mol: SMILES string or RDKit molecule object

Returns: True if molecule passes all criteria

rule_of_three()

python

rule_of_three(mol: Union[str, Chem.Mol]) -> bool

Rule of Three for fragment screening libraries.

Criteria:

Molecular weight ≤ 300 Da
LogP ≤ 3
H-bond donors ≤ 3
H-bond acceptors ≤ 3
Rotatable bonds ≤ 3
Polar surface area ≤ 60 Ų

rule_of_oprea()

python

rule_of_oprea(mol: Union[str, Chem.Mol]) -> bool

Oprea's lead-like criteria for hit-to-lead optimization.

Criteria:

Molecular weight: 200-350 Da
LogP: -2 to 4
Rotatable bonds ≤ 7
Rings ≤ 4

rule_of_cns()

python

rule_of_cns(mol: Union[str, Chem.Mol]) -> bool

CNS drug-likeness rules.

Criteria:

Molecular weight ≤ 450 Da
LogP: -1 to 5
H-bond donors ≤ 2
TPSA ≤ 90 Ų

rule_of_leadlike_soft()

python

rule_of_leadlike_soft(mol: Union[str, Chem.Mol]) -> bool

Soft lead-like criteria (more permissive).

Criteria:

Molecular weight: 250-450 Da
LogP: -3 to 4
Rotatable bonds ≤ 10

rule_of_leadlike_strict()

python

rule_of_leadlike_strict(mol: Union[str, Chem.Mol]) -> bool

Strict lead-like criteria (more restrictive).

Criteria:

Molecular weight: 200-350 Da
LogP: -2 to 3.5
Rotatable bonds ≤ 7
Rings: 1-3

rule_of_veber()

python

rule_of_veber(mol: Union[str, Chem.Mol]) -> bool

Veber's rules for oral bioavailability.

Criteria:

Rotatable bonds ≤ 10
TPSA ≤ 140 Ų

rule_of_reos()

python

rule_of_reos(mol: Union[str, Chem.Mol]) -> bool

Rapid Elimination Of Swill (REOS) filter.

Criteria:

Molecular weight: 200-500 Da
LogP: -5 to 5
H-bond donors: 0-5
H-bond acceptors: 0-10

rule_of_drug()

python

rule_of_drug(mol: Union[str, Chem.Mol]) -> bool

Combined drug-likeness criteria.

Criteria:

Passes Rule of Five
Passes Veber rules
No PAINS substructures

golden_triangle()

python

golden_triangle(mol: Union[str, Chem.Mol]) -> bool

Golden Triangle for drug-likeness balance.

Criteria:

200 ≤ MW ≤ 50×LogP + 400
LogP: -2 to 5

pains_filter()

python

pains_filter(mol: Union[str, Chem.Mol]) -> bool

Pan Assay INterference compoundS (PAINS) filter.

Returns: True if molecule does NOT contain PAINS substructures

Module: medchem.structural

Class: CommonAlertsFilters

Filter for common structural alerts derived from ChEMBL and literature.

Constructor:

python

CommonAlertsFilters()

Methods:

python

__call__(mols: List[Chem.Mol], n_jobs: int = 1, progress: bool = False) -> List[Dict]

Apply common alerts filter to a list of molecules.

Returns: List of dictionaries with keys:

has_alerts: Boolean indicating if molecule has alerts
alert_details: List of matched alert patterns
num_alerts: Number of alerts found

python

check_mol(mol: Chem.Mol) -> Tuple[bool, List[str]]

Check a single molecule for structural alerts.

Returns: Tuple of (has_alerts, list_of_alert_names)

Class: NIBRFilters

Novartis NIBR medicinal chemistry filters.

Constructor:

python

NIBRFilters()

Methods:

python

__call__(mols: List[Chem.Mol], n_jobs: int = 1, progress: bool = False) -> List[bool]

Apply NIBR filters to molecules.

Returns: List of booleans (True if molecule passes)

Class: LillyDemeritsFilters

Eli Lilly's demerit-based structural alert system (275 rules).

Constructor:

python

LillyDemeritsFilters()

Methods:

python

__call__(mols: List[Chem.Mol], n_jobs: int = 1, progress: bool = False) -> List[Dict]

Calculate Lilly demerits for molecules.

Returns: List of dictionaries with keys:

demerits: Total demerit score
passes: Boolean (True if demerits ≤ 100)
matched_patterns: List of matched patterns with scores

Module: medchem.functional

High-level functional API for common operations.

nibr_filter()

python

nibr_filter(mols: List[Chem.Mol], n_jobs: int = 1) -> List[bool]

Apply NIBR filters using functional API.

Parameters:

mols: List of molecules
n_jobs: Parallelization level

Returns: List of pass/fail booleans

common_alerts_filter()

python

common_alerts_filter(mols: List[Chem.Mol], n_jobs: int = 1) -> List[Dict]

Apply common alerts filter using functional API.

Returns: List of results dictionaries

lilly_demerits_filter()

python

lilly_demerits_filter(mols: List[Chem.Mol], n_jobs: int = 1) -> List[Dict]

Calculate Lilly demerits using functional API.

Module: medchem.groups

Class: ChemicalGroup

Detect specific chemical groups in molecules.

Constructor:

python

ChemicalGroup(groups: List[str], custom_smarts: Optional[Dict[str, str]] = None)

Parameters:

groups: List of predefined group names
custom_smarts: Dictionary mapping custom group names to SMARTS patterns

Predefined Groups:

"hinge_binders": Kinase hinge binding motifs
"phosphate_binders": Phosphate binding groups
"michael_acceptors": Michael acceptor electrophiles
"reactive_groups": General reactive functionalities

Methods:

python

has_match(mols: List[Chem.Mol]) -> List[bool]

Check if molecules contain any of the specified groups.

python

get_matches(mol: Chem.Mol) -> Dict[str, List[Tuple]]

Get detailed match information for a single molecule.

Returns: Dictionary mapping group names to lists of atom indices

python

get_all_matches(mols: List[Chem.Mol]) -> List[Dict]

Get match information for all molecules.

Example:

python

group = mc.groups.ChemicalGroup(groups=["hinge_binders", "phosphate_binders"])
matches = group.get_all_matches(mol_list)

Module: medchem.catalogs

Class: NamedCatalogs

Access to curated chemical catalogs.

Available Catalogs:

"functional_groups": Common functional groups
"protecting_groups": Protecting group structures
"reagents": Common reagents
"fragments": Standard fragments

Usage:

python

catalog = mc.catalogs.NamedCatalogs.get("functional_groups")
matches = catalog.get_matches(mol)

Module: medchem.complexity

Calculate molecular complexity metrics.

calculate_complexity()

python

calculate_complexity(mol: Chem.Mol, method: str = "bertz") -> float

Calculate complexity score for a molecule.

Parameters:

mol: RDKit molecule
method: Complexity metric ("bertz", "whitlock", "barone")

Returns: Complexity score (higher = more complex)

Class: ComplexityFilter

Filter molecules by complexity threshold.

Constructor:

python

ComplexityFilter(max_complexity: float, method: str = "bertz")

Methods:

python

__call__(mols: List[Chem.Mol], n_jobs: int = 1) -> List[bool]

Filter molecules exceeding complexity threshold.

Module: medchem.constraints

Class: Constraints

Apply custom property-based constraints.

Constructor:

python

Constraints(
    mw_range: Optional[Tuple[float, float]] = None,
    logp_range: Optional[Tuple[float, float]] = None,
    tpsa_max: Optional[float] = None,
    tpsa_range: Optional[Tuple[float, float]] = None,
    hbd_max: Optional[int] = None,
    hba_max: Optional[int] = None,
    rotatable_bonds_max: Optional[int] = None,
    rings_range: Optional[Tuple[int, int]] = None,
    aromatic_rings_max: Optional[int] = None,
)

Parameters: All parameters are optional. Specify only the constraints needed.

Methods:

python

__call__(mols: List[Chem.Mol], n_jobs: int = 1) -> List[Dict]

Apply constraints to molecules.

Returns: List of dictionaries with keys:

passes: Boolean indicating if all constraints pass
violations: List of constraint names that failed

Example:

python

constraints = mc.constraints.Constraints(
    mw_range=(200, 500),
    logp_range=(-2, 5),
    tpsa_max=140
)
results = constraints(mols=mol_list, n_jobs=-1)

Module: medchem.query

Query language for complex filtering.

parse()

python

parse(query: str) -> Query

Parse a medchem query string into a Query object.

Query Syntax:

Operators: AND, OR, NOT
Comparisons: <, >, <=, >=, ==, !=
Properties: complexity, lilly_demerits, mw, logp, tpsa
Rules: rule_of_five, rule_of_cns, etc.
Filters: common_alerts, nibr_filter, pains_filter

Example Queries:

python

"rule_of_five AND NOT common_alerts"
"rule_of_cns AND complexity < 400"
"mw > 200 AND mw < 500 AND logp < 5"
"(rule_of_five OR rule_of_oprea) AND NOT pains_filter"

Class: Query

Methods:

python

apply(mols: List[Chem.Mol], n_jobs: int = 1) -> List[bool]

Apply parsed query to molecules.

Example:

python

query = mc.query.parse("rule_of_five AND NOT common_alerts")
results = query.apply(mols=mol_list, n_jobs=-1)
passing_mols = [mol for mol, passes in zip(mol_list, results) if passes]

Module: medchem.utils

Utility functions for working with molecules.

batch_process()

python

batch_process(
    mols: List[Chem.Mol],
    func: Callable,
    n_jobs: int = 1,
    progress: bool = False,
    batch_size: Optional[int] = None
) -> List

Process molecules in parallel batches.

Parameters:

mols: List of molecules
func: Function to apply to each molecule
n_jobs: Number of parallel workers
progress: Show progress bar
batch_size: Size of processing batches

standardize_mol()

python

standardize_mol(mol: Chem.Mol) -> Chem.Mol

Standardize molecule representation (sanitize, neutralize charges, etc.).

Common Patterns

Pattern: Parallel Processing

All filters support parallelization:

python

# Use all CPU cores
results = filter_object(mols=mol_list, n_jobs=-1, progress=True)

# Use specific number of cores
results = filter_object(mols=mol_list, n_jobs=4, progress=True)

Pattern: Combining Multiple Filters

python

import medchem as mc

# Apply multiple filters
rule_filter = mc.rules.RuleFilters(rule_list=["rule_of_five"])
alert_filter = mc.structural.CommonAlertsFilters()
lilly_filter = mc.structural.LillyDemeritsFilters()

# Get results
rule_results = rule_filter(mols=mol_list, n_jobs=-1)
alert_results = alert_filter(mols=mol_list, n_jobs=-1)
lilly_results = lilly_filter(mols=mol_list, n_jobs=-1)

# Combine criteria
passing_mols = [
    mol for i, mol in enumerate(mol_list)
    if rule_results[i]["passes"]
    and not alert_results[i]["has_alerts"]
    and lilly_results[i]["passes"]
]

Pattern: Working with DataFrames

python

import pandas as pd
import datamol as dm
import medchem as mc

# Load data
df = pd.read_csv("molecules.csv")
df["mol"] = df["smiles"].apply(dm.to_mol)

# Apply filters
rfilter = mc.rules.RuleFilters(rule_list=["rule_of_five", "rule_of_cns"])
results = rfilter(mols=df["mol"].tolist(), n_jobs=-1)

# Add results to dataframe
df["passes_ro5"] = [r["rule_of_five"] for r in results]
df["passes_cns"] = [r["rule_of_cns"] for r in results]

# Filter dataframe
filtered_df = df[df["passes_ro5"] & df["passes_cns"]]