Bio Reaction Enumeration

Version Compatibility

Reference examples tested with: RDKit 2024.09+, mmpdb 3.1+, scikit-learn 1.4+, numpy 1.26+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Reaction Enumeration

Generate virtual libraries by applying reaction SMARTS to building blocks, enumerate analog series via matched molecular pairs, decompose into R-groups for SAR modeling, or extract transformations from reaction data. Reaction enumeration sits at the intersection of medicinal chemistry, lead optimization, and de novo design. The two key operations: transform (apply known rxn to make new compounds) and mine (extract rules from observed analog series). RDKit's reaction SMARTS handles the former; mmpdb / Free-Wilson handle the latter.

For retrosynthetic planning (target-to-starting-material decomposition), see chemoinformatics/retrosynthesis. For ML-driven design, see chemoinformatics/generative-design. For scaffold-based design, see chemoinformatics/scaffold-analysis.

Operation Taxonomy

Reaction SMARTS Basics

A reaction SMARTS is reactants >> products with atom maps [atom:idx] tracking atoms through the transformation:

from rdkit.Chem import AllChem, Chem

amide = AllChem.ReactionFromSmarts(
    '[C:1](=[O:2])O.[N:3]>>[C:1](=[O:2])[N:3]'
)

errors = amide.Validate()
print(errors)

Atom mapping rules:

• Atoms with the same map index [C:1] in both reactant and product are tracked
• Maps must be unique within each reactant/product
• Unmapped atoms are added to or removed from the product
• Bond orders may change; map index preserves identity

Common error: Leaving an atom unmapped causes RDKit to either lose or duplicate it.

Common Reaction Templates

REACTIONS = {
    'amide_coupling': '[C:1](=[O:2])O.[N:3]>>[C:1](=[O:2])[N:3]',
    'reductive_amination': '[C:1](=O).[NH2:2]>>[CH:1][NH:2]',
    'suzuki': '[c:1][Br].[c:2][B](O)O>>[c:1][c:2]',
    'buchwald_hartwig': '[c:1][Br].[NH:2]>>[c:1][N:2]',
    'sn2_substitution': '[CH:1][Br].[N:2]>>[CH:1][N:2]',
    'sonogashira': '[c:1][Br].[CH:2]#[C:3]>>[c:1][C:2]#[C:3]',
    'click_chemistry': '[N-:1]=[N+:2]=[N:3][CH2:4].[CH:5]#[C:6]>>[N:3]1[N:2]=[N:1][C:6]=[C:5]1[CH2:4]',
    'esterification': '[C:1](=[O:2])O.[OH:3][C:4]>>[C:1](=[O:2])[O:3][C:4]',
    'urea_formation': '[N:1]=C=O.[NH:2]>>[N:1]C(=O)[N:2]',
    'sulfonamide': '[S:1](=O)(=O)Cl.[NH:2]>>[S:1](=O)(=O)[N:2]',
}

These are templates; real reactions need stereo, protecting-group, and chemoselectivity considerations. For production library enumeration, use validated templates from rxnmapper or vendor catalogs.

Combinatorial Library Enumeration

Goal: Generate every (R1, R2, ..., Rn) product combination from sets of building blocks.

Approach: Cartesian product of reactant lists; apply reaction SMARTS; sanitize + deduplicate.

from itertools import product
from rdkit import Chem
from rdkit.Chem import AllChem

def enumerate_library(rxn_smarts, reactant_lists, mw_max=600):
    rxn = AllChem.ReactionFromSmarts(rxn_smarts)
    if rxn.Validate()[0] != 0:
        raise ValueError(f'Invalid reaction: {rxn_smarts}')

    seen = set()
    products = []
    for combo in product(*reactant_lists):
        mols = [Chem.MolFromSmiles(s) for s in combo]
        if None in mols:
            continue

        for prod_tuple in rxn.RunReactants(tuple(mols)):
            for prod in prod_tuple:
                try:
                    Chem.SanitizeMol(prod)
                    smi = Chem.MolToSmiles(prod)
                    if smi in seen:
                        continue
                    if Chem.Descriptors.MolWt(prod) > mw_max:
                        continue
                    seen.add(smi)
                    products.append(smi)
                except Exception:
                    continue
    return products

Scaling: For a 100x100x100 enumeration (1M products), parallelize with multiprocessing. For 1k x 1k x 1k (1B products), use a streaming approach + filter before materializing.

RECAP Fragmentation

RECAP (Lewell 1998) breaks molecules at retrosynthetically reasonable bonds into reusable fragments.

from rdkit.Chem import Recap

mol = Chem.MolFromSmiles('c1ccc(C(=O)Nc2ccc(F)cc2)cc1')
hier = Recap.RecapDecompose(mol)
fragments = list(hier.GetLeaves().keys())

RECAP bond types: amide, ester, ether, amine, urea, olefin, quaternary nitrogen, sulfonamide. Use cases: building-block library generation, scaffold-decoration enumeration.

BRICS Fragmentation

BRICS (Degen 2008) is an extension of RECAP with more bond types. Better fragment coverage; more fragments per molecule.

from rdkit.Chem import BRICS

mol = Chem.MolFromSmiles('CCN(CC)c1ccc(C(=O)NC2CCCC2)cc1')
fragments = BRICS.BRICSDecompose(mol)

builder = BRICS.BRICSBuild([Chem.MolFromSmiles(f) for f in fragments])
new_mols = [next(builder) for _ in range(10)]

BRICSDecompose produces SMILES with [<dummy>] attachment points; BRICSBuild recombines fragments at these dummies.

R-Group Decomposition

Goal: Given a set of compounds sharing a scaffold, extract the R-group at each attachment point into a tabular SAR matrix.

Approach: Define scaffold with [*:1], [*:2] placeholders; RDKit matches each compound and extracts R-groups.

from rdkit.Chem import rdRGroupDecomposition as rgd
from rdkit import Chem

scaffold = Chem.MolFromSmiles('c1ccc(-[*:1])cc1-[*:2]')

mols = [Chem.MolFromSmiles(smi) for smi in [
    'c1ccc(C)cc1F',
    'c1ccc(CC)cc1Cl',
    'c1ccc(CCC)cc1Br',
]]

decomp, _ = rgd.RGroupDecompose([scaffold], mols, asSmiles=True)

decomp is a list of dicts {'Core': scaffold_smi, 'R1': r1_smi, 'R2': r2_smi}. Combined with activity column, enables Free-Wilson.

Matched Molecular Pairs Analysis (MMPA)

MMPA (Hussain & Rea 2010) extracts SAR rules from compound pairs differing by a single transformation.

mmpdb fragment data.smi -o data.fragments
mmpdb index data.fragments -o data.mmpdb
mmpdb transform --smiles 'COc1ccccc1' data.mmpdb

mmpdb produces a database of transformations + statistics on activity changes.

Use case: Lead optimization. Given a hit, ask "what transformations have improved similar series?" Apply top-ranked transformations to generate analog suggestions.

Context-based MMPA (Awale 2024): condition rules on local chemical context (e.g., "Me→F adjacent to amide"). Outperforms classical MMPA on CYP1A2 inhibition reduction.

Free-Wilson Analysis

Goal: Decompose activity into additive R-group contributions.

Approach: Linear regression with R-group identity as binary features.

import pandas as pd
from sklearn.linear_model import Ridge

def free_wilson(decomp_results, activity_col='pIC50'):
    df = pd.DataFrame(decomp_results)
    r_groups = pd.get_dummies(df[['R1', 'R2']], prefix=['R1', 'R2'])
    X = r_groups.values
    y = df[activity_col].values
    model = Ridge(alpha=0.1).fit(X, y)
    contributions = dict(zip(r_groups.columns, model.coef_))
    return contributions, model.intercept_

Trade-off: Free-Wilson assumes additivity (R1 contribution independent of R2). Real SAR has interactions; Free-Wilson predictions for un-synthesized combinations are biased when synergy exists. Use as a first-pass model for analog prioritization; validate with QSAR.

Template Extraction from Reaction Data

Goal: Given an atom-mapped reaction SMILES, extract a generalizable SMARTS template.

Approach: Use rxnmapper (Schwaller 2021) for atom mapping, then RDKit reaction template extraction.

from rxnmapper import RXNMapper

mapper = RXNMapper()
rxns = ['CCO.OC(=O)c1ccccc1>>CCOC(=O)c1ccccc1']
results = mapper.get_attention_guided_atom_maps(rxns)
mapped_smiles = results[0]['mapped_rxn']

After atom-mapping, RDKit can extract a template via ChemicalReaction.GetReactionTemplateFromMappedReaction (custom implementation; see Coley 2019).

Per-Tool Failure Modes

Reaction SMARTS -- atom mapping mismatch

Trigger: Map index appears on reactant but not product.

Mechanism: RDKit treats unmapped atoms as deleted from product; an atom that was meant to be preserved disappears if its map index is missing.

Symptom: Products missing expected atoms; valences wrong; sanitize fails.

Fix: Validate with rxn.Validate(); manually inspect mapping; use Reaction Atom Mapping Number column 2.

RECAP/BRICS -- over-fragmentation

Trigger: Highly substituted molecule with many breakable bonds.

Mechanism: Default bond list breaks at every retrosynthetic position; one molecule yields tens of fragments.

Symptom: Building-block enumeration explodes; many small irrelevant fragments.

Fix: Filter fragments by MW (>=80 Da), heavy atom count (>=4); use only meaningful fragments downstream.

MMPA -- insufficient pair count

Trigger: mmpdb on small dataset (<500 compounds, <50 actives).

Mechanism: MMPA needs ≥10 pairs per transformation to yield a statistically meaningful delta(activity).

Symptom: Transformations report with N=1-3 pairs; effect sizes erratic.

Fix: Filter to transformations with N>=10; supplement with literature SAR knowledge.

Free-Wilson -- non-additive interactions

Trigger: R1 and R2 interact through hydrogen bonding, steric clash, or electronic effects.

Mechanism: Free-Wilson is purely additive; cannot capture R1+R2 synergy.

Symptom: Predicted activities for un-synthesized combinations are biased low for synergistic pairs.

Fix: Use Free-Wilson as first-pass screen; validate predictions with QSAR (random forest, chemprop) which captures interactions.

R-group decomposition -- multiple scaffolds

Trigger: Compound matches multiple scaffold templates.

Mechanism: RGroupDecompose returns the first matching scaffold; ambiguous SAR series.

Symptom: Same compound's R-groups differ between runs.

Fix: Specify scaffold unambiguously; use only compounds matching one scaffold.

Reaction enumeration -- combinatorial explosion

Trigger: Large building-block sets (1k x 1k = 1M products).

Mechanism: Cartesian product * RunReactants is O(N^d) where d is reactant count.

Symptom: Memory blowup, multi-hour runtime.

Fix: Pre-filter building blocks; stream products to file rather than list; use mmpdb-style sparse enumeration only for valid pairings.

Reconciliation: Free-Wilson vs MMPA

Both methods derive R-group rules but from different perspectives:

• Free-Wilson: linear regression on assembled SAR table; gives R-group contributions
• MMPA: transformation-based; gives delta(activity) for each substitution

If they agree on direction (Me→F improves activity), high confidence. If they disagree, investigate non-additive interactions or look for context dependence in MMPA.

Common Errors

References

• Hartenfeller et al., J. Cheminformatics 4:38 (2012) -- DOGS rule-based library design.
• Lewell et al., J. Chem. Inf. Comput. Sci. 38:511 (1998) -- RECAP.
• Degen et al., ChemMedChem 3:1503 (2008) -- BRICS fragmentation.
• Hussain & Rea, J. Chem. Inf. Model. 50:339 (2010) -- MMPA.
• Dossetter et al., Drug Discov. Today 18:724 (2013) -- Practical MMPA in lead optimization.
• Free & Wilson, J. Med. Chem. 7:395 (1964) -- Original Free-Wilson.
• Schwaller et al., Sci. Adv. 7:eabe4166 (2021) -- rxnmapper.
• Coley et al., Chem. Sci. 10:370 (2019) -- Reaction template extraction.

Related Skills

• chemoinformatics/molecular-io - Read/write reaction SMILES
• chemoinformatics/substructure-search - SMARTS pattern matching
• chemoinformatics/scaffold-analysis - Bemis-Murcko scaffolds for R-decomp
• chemoinformatics/molecular-descriptors - Featurize products
• chemoinformatics/admet-prediction - Filter enumerated products
• chemoinformatics/retrosynthesis - Reverse direction (target → starting materials)
• chemoinformatics/generative-design - Generative alternatives to template enumeration
• chemoinformatics/qsar-modeling - Validate Free-Wilson predictions