AI-driven Drug Optimization
AI-accelerated DMTA services for small-molecule drug optimization
MedMap combines AI molecule generation, ADMET prediction, intelligent docking, PK/PD simulation and medicinal chemistry insight into a closed-loop Design–Make–Test–Analyze workflow. The service helps discovery teams prioritize better candidates, rescue liabilities and reduce costly iteration cycles.
What makes the service different
A platform built around real lead-optimization decisions, not one-off molecule scoring.
Closed-loop DMTA
Every design round learns from assay, docking and property results, creating an auditable optimization trail for project teams.
42-endpoint ADMET-AI
ML-based prediction across solubility, permeability, CYP, hERG, AMES, DILI, HLM clearance and other DMPK/toxicity endpoints.
Patent-aware optimization
Scaffold hopping, bioisosteric edits and FTO pre-screening help teams explore chemical space while managing IP risk.
Service capabilities
We support lead optimization programs from target and seed-structure intake through AI design, ranking, liability analysis and experimental-cycle planning.
- • Molecular generation with MolGPT / REINVENT / diffusion / RDKit workflows
- • QSAR and activity prediction using graph neural networks and Chemprop-style models
- • Docking strategy selection across ligand classes with pose-confidence interpretation
- • Defect-driven Smart Optimize for ADMET, toxicity, docking and selectivity liabilities
- • PK/PD and virtual patient simulation to support candidate prioritization
- • Project knowledge base that captures SAR, liabilities, motifs and decision rationale
Typical engagement outputs
Candidate design report
Ranked analogs with potency, ADMET, docking, synthesis and FTO summaries.
Liability rescue plan
Targeted chemical edits for hERG, CYP, AMES, solubility, clearance and docking weaknesses.
DMTA cycle roadmap
Actionable recommendations for synthesis, assay design and next-round optimization.
Designed for real discovery teams
Medicinal Chemists
Generate and prioritize analogs with interpretable SAR and defect-aware edits.
DMPK Scientists
Evaluate multi-endpoint ADMET risks early and guide rescue strategies.
Project Leaders
Use a transparent decision trail to focus resources on the most promising candidates.