AI-MEDCRAFT™ · FEATURED ON THE COVER OF JCIM · APRIL 2026
AI-guided drug design across the full pipeline — not just one stage
Most AI tools optimize one property at a time, then fail at the trade-off. AI-MedCraft balances potency, selectivity, solubility, and safety simultaneously — across 28+ validated strategies spanning hit identification from hit identification through preclinical decision support.
SEE IT IN ACTION
Watch AI-MedCraft at work
A short walkthrough of the platform — from selecting a design strategy to reviewing ranked candidates.
Prefer to talk through it? Book a live demo
Multi-objective drug design, at every stage
WHAT AI-MEDCRAFT DOES
AI-MedCraft plugs into the full drug development lifecycle — from the first hit to regulatory readiness. 28+ validated strategies, one interface, one data model, one vendor relationship.
KEY CAPABILITIES
Features that drive outcomes
Every capability is designed to produce drug design decisions you can act on — not just predictions.
01
Strategy Selection
The user picks a medicinal chemistry intent — structural innovation (new scaffolds, backbone morphing), property optimization (solubility rescue, CNS design, ADMET), or target precision (isoform selectivity, off-target de-risking, polypharmacology). No reward engineering required.
02
Adaptive Pareto-Guided RL
A hybrid Transformer + LSTM backbone pre-trained on 1.5M drug-like molecules (ChEMBL, ZINC) generates candidates. Objective weights rebalance dynamically based on population-level performance — no collapse to dominant objectives, no manual tuning.
03
Physics-Aware Scoring
When structural data are available, docking, pharmacophore alignment, and interaction-pattern analysis are integrated directly into the reward signal. Proposed analogs aren't just statistically plausible — they're physically meaningful.
04
Ranking & Analysis
Candidates are ranked by Pareto dominance and presented through interactive trade-off visualizations — parallel coordinates, Pareto fronts, similarity landscapes. Export ranked, synthesis-ready SMILES to your chemistry team.
PEER-REVIEWED BENCHMARK
Rigorously benchmarked and peer-reviewed
Scalar-reward and weighted-sum reinforcement learning — the approach underlying most open-source and commercial generative chemistry tools — systematically collapses toward whichever objective dominates the reward signal.
The resulting Pareto fronts tell the whole story. REINVENT 4's top-ranked solutions (red stars) are pinned to the bottom-right corner: high affinity, negligible solubility. AI-MedCraft's Pareto front spans the full objective space — with top hits sitting in genuinely useful territory.
Same starting compound. Same objectives. Same computational budget. Published, reproducible, and decisive.
Test case: Structure-based solubility rescue
Compound: GDC-0834 (BTK inhibitor)
Structure: PDB 5P9F
Matched settings: objectives, normalization,
compute, 1,000 training steps each
Hardware: NVIDIA L40S GPU (48GB) + 8 CPUs
PROVEN ON REAL COMPOUNDS
Two rescued compounds. One platform.
Both case studies are published, peer-reviewed, and reproducible from the Zenodo archive. Each demonstrates AI-MedCraft on a distinct class of pharma-relevant problem.
GDC-0834 · Bruton's tyrosine kinase · PDB 5P9F
GDC-0834 showed excellent BTK activity but was discontinued for poor solubility — experimental LogS of −4.93 (0.007 mg/mL). AI-MedCraft was given one instruction: improve solubility without breaking binding.
Top analogs reached predicted LogS of −2.5 to −3 (roughly two orders of magnitude more soluble) while preserving the hinge-binder geometry, validated by 30 ns molecular dynamics and MM-PBSA binding free energy analysis.
CASE STUDY 01 · SOLUBILITY RESCUE
Rescuing a shelved BTK inhibitor.
GDC-0834 · Bruton's tyrosine kinase PDB 5P9F
GDC-0834 showed excellent BTK activity but was discontinued for poor solubility — experimental LogS of −4.93 (0.007 mg/mL). AI-MedCraft was given one instruction: improve solubility without breaking binding.
Top analogs reached predicted LogS of −2.5 to −3 (roughly two orders of magnitude more soluble) while preserving the hinge-binder geometry, validated by 30 ns molecular dynamics and MM-PBSA binding free energy analysis.
CASE STUDY 02 · OFF-TARGET DE-RISKING
Keeping the drug. Removing the side effects.
Efavirenz · HIV-1 reverse transcriptase 5-HT2A
Efavirenz is an effective antiviral linked to serious neuropsychiatric side effects — it accidentally binds the serotonin 5-HT2A receptor in the brain. AI-MedCraft was asked to retain RT engagement while disrupting the serotonin-mimetic binding mode.
Of the top 8 analogs validated with full MD + MM-PBSA simulations, 7 showed greater on-target / off-target energetic separation than Efavirenz itself, with 5 showing significantly higher selectivity.
HOW IT WORKS
From a compound to ranked candidates, in one run.
No coding. No manual weight-tuning. A medicinal chemist picks a strategy, uploads a molecule, and gets ranked, synthesis-ready candidates.
WHO IT'S FOR
Built for teams that balance trade-offs every day.
HOW WE HANDLE YOUR SCIENCE
Your compounds stay yours.
AI-MedCraft was built by computational chemists who've worked inside pharma — we understand what IP protection really means in this field.
ENGAGEMENT MODELS
Three ways to work with us.
We price to the scope of the engagement, not a per-seat number. Tell us which shape fits — we'll take it from there.
SEE IT ON YOUR COMPOUND
Book a 30-minute demo. Bring a problem.
Send us a compound from your pipeline — a shelved lead, an off-target-plagued candidate, a solubility problem you've been fighting. We'll run AI-MedCraft on it and walk you through the results. No commitment.