admet-prediction

Goal

Compute ADMET-relevant physicochemical descriptors and rule-based drug-likeness heuristics from SMILES strings using RDKit.

This skill reports:

• Core descriptors: molecular weight (average and exact), Wildman-Crippen cLogP, TPSA, HBD/HBA, rotatable bonds, ring counts, aromatic rings, heavy atoms, fractionCSP3, molar refractivity.
• Heuristics:
  • Lipinski Rule of Five (Ro5) compliance (≤ 1 violation) as a permeability/absorption triage heuristic.
  • Veber oral bioavailability heuristic (RB ≤ 10 and TPSA ≤ 140 Ų; plus reporting the alternative HBD+HBA ≤ 12 condition).
  • QED (Quantitative Estimate of Drug-likeness) score.

Note: This does not predict experimental ADMET endpoints (e.g., clearance, CYP inhibition, hERG, Ames, etc.). It is an early-stage physchem/heuristics screen.

Instructions

The drugdisc MCP server provides a compute_molecular_descriptors tool that can be called directly:

Single molecule analysis:

mcp_drugdisc_compute_molecular_descriptors(
    smiles="CC(=O)Oc1ccccc1C(=O)O",
    output_file="aspirin_admet.json"
)

Batch analysis from a SMILES file:

mcp_drugdisc_compute_molecular_descriptors(
    smiles_file=".agents/skills/drug-admet-prediction/examples/compounds.smi",
    output_file="batch_admet.json"
)

With S/P-inclusive TPSA:

mcp_drugdisc_compute_molecular_descriptors(
    smiles="OC(=O)P(=O)(O)O",
    include_sandp_tpsa=True,
    output_file="foscarnet_admet.json"
)

Examples

Example compounds.smi:

CN1C=NC2=C1C(=O)N(C(=O)N2C)C	caffeine
CC(=O)Oc1ccccc1C(=O)O	aspirin
CC(C)Cc1ccc(cc1)C(C)C(=O)O	ibuprofen

Run:

mcp_drugdisc_compute_molecular_descriptors(
    smiles_file=".agents/skills/drug-admet-prediction/examples/compounds.smi",
    output_file="drug_admet.json"
)

Constraints

• MCP Server: Requires drugdisc MCP server
• Dependencies: RDKit (Chem, Descriptors, Lipinski, Crippen, QED)
• Scope: Outputs physchem descriptors + rule-based heuristics only; not ML/experimental ADMET prediction
• Ro5 interpretation: A "pass" is defined here as ≤ 1 violation (common industry convention)
• Veber interpretation: Primary check uses TPSA ≤ 140 Ų and rotatable bonds ≤ 10, and additionally reports the alternative (HBD + HBA ≤ 12) criterion
• Standardization: If SMILES contains multiple fragments (e.g., salts, "."), results are reported but flagged with a warning; consider desalting/neutralization upstream for library triage
• TPSA option: By default, TPSA uses RDKit's default behavior (no S/P); include_sandp_tpsa=True includes S/P contributions
• Two HBA definitions, both reported: hba is rdMolDescriptors.CalcNumHBA, the strict SMARTS acceptor count that excludes amide and pyrrole-type N with delocalised lone pairs (caffeine = 3: two carbonyl O plus one imidazole =N-). hba_lipinski is rdMolDescriptors.CalcNumLipinskiHBA, the raw N+O count Lipinski 1997 specified (caffeine = 6). Ro5 is scored on hba_lipinski, per the original paper. Do not call the Lipinski.NumHAcceptors alias: its meaning changed between rdkit 2025.09.4 and 2025.09.6 (caffeine 6 -> 3), so results computed through it are not comparable across environments. hba inherits that library change and will read 6 on rdkit <= 2025.09.4 and 3 on >= 2025.09.6; hba_lipinski is stable on both.

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Author: Matthew Cox Contact: GitHub @mcox3406