Generate optimized drug candidates in minutes - not weeks
Each scaffold hop used to mean a week of manual profiling - ADMET in one tool, compliance in another, comparison in a spreadsheet. Now scaffold hopping and AI optimization generate variants with ADMET, compliance, and patent risk inline. Eliminate weak candidates before you spend time docking or simulating them.
“Optimize this lead for better oral bioavailability and lower hERG liability while maintaining EGFR selectivity.”
How it works
Submit a lead and objectives
Provide a SMILES and optional property targets - QED, LogP, molecular weight, similarity threshold. Choose scaffold hopping for structural diversity or MolMIM for property-directed fine-tuning.
Variants generated and enriched
Scaffold hopping swaps ring systems (benzene↔pyridine, cyclohexane↔piperidine). MolMIM generates AI-guided variants targeting your profile. Every variant auto-enriched with ADMET predictions and FAVES compliance.
Ranked variants with full profiles
You receive variants sorted by property match, each with Tanimoto similarity to seed, patent risk assessment, compliance status, and complete ADMET profile. Ready for docking.
Proof
Two paths: lead_optimization (RDKit scaffold hopping, 30+ ring pairs) and optimize_molecule (NVIDIA MolMIM, property-directed).
Post-optimization enrichment: chem-props (SA/properties) + addie-models (31 ADMET models) run in parallel. FAVES auto-screens every variant.
Patent risk via Tanimoto similarity to Pinecone patent index. Each variant returns compliance_status, tanimoto_to_seed, and patent_risk.
Use this when you need to
Improve ADMET without losing potency
Explore new scaffolds quickly
Reduce toxicity risk before committing to docking
Assess patent landscape alongside structural changes
Generate optimized candidates - ADMET inline
Scaffold hopping + AI optimization. Eliminate weak candidates before docking.