Matched Molecular Pair Analysis (MMPA)

MMPA identifies pairs of molecules (A, B) that differ by a single structural transformation (R₁ → R₂) at one site while sharing an identical molecular context. Aggregating ΔProperty across hundreds of such pairs extracts robust, context-free SAR rules.

When to Use This Skill

• Extract SAR rules from a compound dataset (e.g., which H→F swap improves metabolic stability?)
• Identify activity cliffs (large ΔActivity for small structural change)
• Find bioisosteric replacements supported by experimental data
• Predict property changes for a proposed structural modification
• Generate analogue libraries from a lead compound using proven transforms
• Prioritize which substituent to try next based on MMPA-derived rules

Core Concept

Molecule A:  [Context]-[R₁]   →   transform: R₁ → R₂
Molecule B:  [Context]-[R₂]

ΔProperty = P(B) - P(A)

SAR rule: "R₁ → R₂ causes ΔlogP = +0.45 ± 0.12 (N=18 pairs)"

Fragmentation (single-cut):

• Variable part: the part that changes between A and B
• Context: the shared skeleton (everything outside the cut bond)
• Represented via SMIRKS: [R₁:1]>>[R₂:1] at attachment point

Double-cut: both variable parts and a central linker can vary — rarer, more specific.

Quick Start — mmpdb 4

pip install mmpdb
# or: conda install -c conda-forge mmpdb

# Full pipeline: SMILES file → SAR rules
mmpdb fragment compounds.smi -o fragments.h5
mmpdb index fragments.h5 -o mmpdb.db

# Load experimental properties (CSV with: smiles, id, prop1, prop2)
mmpdb loadprops mmpdb.db properties.csv

# Query: what transforms improve LogD?
mmpdb transform --smiles "c1ccc(cc1)C(=O)O" mmpdb.db \
    --property LogD \
    --min-pairs 3 \
    -o transform_results.csv

# Analyze SAR rules for a property
mmpdb analyze mmpdb.db --property pIC50 -o sar_rules.csv

Quick Start — Programmatic (RDKit + mmpdb Python API)

import mmpdblib
from mmpdblib import do_fragment, do_index

# Fragment a SMILES list programmatically
from mmpdblib.analysis_algorithms import find_mmps

smiles_dict = {
    "mol_A": "c1ccc(CC)cc1",   # ethylbenzene
    "mol_B": "c1ccc(CF)cc1",   # fluoromethylbenzene
    "mol_C": "c1ccc(CCl)cc1",  # chloromethylbenzene
}

# Find matched molecular pairs
pairs = find_mmps(list(smiles_dict.values()), list(smiles_dict.keys()))
# pairs: list of (id1, id2, transform_SMIRKS, context_SMILES)

Router — What to Read

Task	Reference
MMP definition, fragmentation theory, SMIRKS transforms, property cliffs, statistical framework	references/mmpa-theory.md
mmpdb 4 CLI: fragment → index → loadprops → transform → analyze, full SAR workflow	references/mmpdb-workflow.md
RDKit programmatic MMP generation: bond cutting, attachment points, pair enumeration	references/rdkit-fragmentation.md
SAR delta statistics, activity cliff detection, bioisostere tables, visualization	references/sar-analysis.md
Applying transforms to query molecule, analogue library generation, integration with design tools	references/transform-application.md

Software Stack

Package	Install	Role
mmpdb	pip install mmpdb	Core MMPA engine (AZ, Apache 2.0)
rdkit	conda/pip	Fragmentation, SMILES parsing, visualization
pandas	pip install pandas	SAR table analysis
seaborn / matplotlib	pip	Activity cliff heatmaps, ΔP distributions
mols2grid	pip install mols2grid	Interactive molecule grid visualization

Key Concepts

Term	Definition
MMP	Two molecules differing by exactly one transformation at one site
Transform	SMIRKS notation: [*:1]>>[*:1] where [*:1] = attachment point
Variable part	The fragment that differs between the two molecules
Context	Shared molecular scaffold (everything outside the cut bond)
ΔProperty	P(B) − P(A) for any measured property
SAR rule	A transform + aggregated ΔP statistics across N≥3 pairs
Activity cliff	Large

Key Pitfalls

• mmpdb 2 vs mmpdb 4: many tutorials use old API (mmpdb.make_index); v4 uses CLI subcommands (mmpdb fragment/index/loadprops)
• Single-cut only: mmpdb default cuts one bond; avoid multi-cut for first pass (combinatorial explosion)
• Variable part size limit: default max_heavies=10 for variable part; increase with --max-variable-heavies 13 for larger changes
• N < 3 pairs → unreliable rule: always filter output to num_pairs >= 3
• Chirality: mmpdb ignores chirality by default; use --stereo flag if needed
• Property units: ΔlogP and ΔpIC50 are in log units; ΔCl (clearance) in mL/min/kg — always report units

Related Skills

• rdkit — SMILES I/O, substructure matching, property calculation (QED, LogP)
• generative-design — MMPA-guided focused library generation (apply transforms at scale)
• pharmacophore — combine MMPA bioisostere rules with pharmacophore constraints
• docking — score MMPA-generated analogues via Vina/Gnina
• scientific-skills:chembl-database — ChEMBL as MMPA input dataset for literature SAR mining