Development of an AI Virtual Screening System for Drug Discovery
Identifying active molecules from a library of billions of compounds is a key challenge in drug discovery. Classical HTS requires weeks and millions of dollars. AI virtual screening Virtual screening changes the rules: we build end-to-end systems turnkey, from selecting molecular fingerprints to deployment on a GPU cluster. In one project, we found 12 active hits with a hit rate of 14% in 3 weeks, compared to 0.5% in random screening, cutting the budget by 20 times. Our solutions reduce hit discovery time from months to days — assess your project by contacting us.
We rely on 10 years of experience in cheminformatics and MLOps. We guarantee quality: enrichment factor EF@1% > 50, prediction accuracy within model confidence intervals. For billion-scale screening, we use distributed infrastructure: GPU clusters with 8–32 A100, Triton Inference Server, and ONNX Runtime for model inference.
Virtual Screening Methods
Ligand-based screening (LBVS)
Uses information about known active molecules. If we have a set of active molecules against a target, we search for similar ones.
- Similarity search: molecular fingerprints (Morgan/ECFP, MACCS) + Tanimoto coefficient. Fast, scales to billions.
- Pharmacophore modeling: identifying key 3D pharmacophoric points of active molecules → searching for molecules with the same spatial arrangement.
- QSAR (Quantitative Structure-Activity Relationship): ML model predicts pIC50 from structural features.
Structure-based screening (SBVS)
Uses the 3D structure of the target protein. Molecules are docked into the active site.
The bottleneck of classical SBVS: docking one molecule takes seconds → 1 billion molecules = 30 years of CPU. AI solutions:
- Surrogate ML models: fast ML scoring (milliseconds) replaces docking as a pre-filter.
- Neural Network Potentials for scoring: more accurate binding evaluation.
- Ultra-large scale docking: Glide SP, DOCK6 optimized for 10⁹ scales with proper infrastructure.
How AI Screening Surpasses Classical Docking?
Classical docking (SBVS) is computationally expensive: one molecule takes seconds of CPU. AI surrogate models reduce time to milliseconds while preserving accuracy. In a test project, we replaced docking for a pre-filter: speed increased 1000x, AUC ROC remained at 0.85. Method comparison:
| Method | Time per 1M molecules | Accuracy (AUC) |
|---|---|---|
| Classical docking | ~30 days (CPU) | 0.8–0.9 |
| ML surrogate | ~1 hour (GPU) | 0.75–0.85 |
| Combined funnel | ~3 days (GPU) | 0.85–0.95 |
Ultra-Large Library Screening
Enamine REAL Space: 36 billion synthetically accessible molecules. An effective strategy is hierarchical funnel plus generative screening.
Molecular Embeddings
Training an encoder (Transformer or GNN) for compact vector representations of molecules. Nearest neighbor search in embedding space in milliseconds. FAISS for indexing billions of vectors.
Generative Screening (Make-on-Demand)
Instead of screening a pre-built library, generate new molecules with desired properties in synthetically accessible chemical space. Reinvent, SAFE (IUPAC), Synthetically Accessible Drug Space.
Hierarchical Funnel Approach
Billion-scale library
→ Fast ML pre-filter (Tanimoto/embedding): 10⁹ → 10⁶
→ QSAR activity filter: 10⁶ → 10⁵
→ Fast docking: 10⁵ → 10⁴
→ Accurate docking (Glide XP): 10⁴ → 10³
→ FEP calculation: 10³ → 100
→ Synthesis & experimental validation: ~50
Each level: slower but more accurate method. Throughput of each level matched to the capacity of the next.
Real-world funnel pipeline example
In a real project for a pharma company, we used: Tanimoto pre-filter on 10⁸ molecules, then a LightGBM QSAR model, then Glide SP on 10⁵, then Glide XP on 10⁴. Full cycle: 3 days on 32 A100. Final hit rate: 8%.Why Active Learning Is More Effective Than Random Screening?
Traditional VS: random sample for testing. Active Learning — the ML model selects which molecules are most informative for the next iteration of experiments.
Cycle:
- Initial dataset (1000 molecules with measured activity)
- Train surrogate model
- Acquisition function picks next 100 molecules (Expected Improvement, UCB)
- Synthesis + test
- Repeat
Result: reduces required syntheses by 5–20 times compared to random screening to find active hits. In one project, we achieved a hit rate of 12% with active learning vs 1% with random — budget saving of 10x.
Screening Effectiveness Metrics
| Metric | Description |
|---|---|
| Enrichment Factor (EF) | How many times more active molecules in top-X% than in random selection |
| AUC (ROC) | Discrimination of active / inactive |
| BEDROC | Weighted metric emphasizing top hits |
| Hit Rate | % active among synthesized candidates |
Target: EF@1% > 50 (top 1% of molecules contain 50 times more actives than random).
Infrastructure for billion-scale screening: GPU cluster (8–32 A100), distributed inference with Ray or Dask, object storage for molecular data. Full screening of 1B molecules: 24–72 hours depending on depth of analysis.
What's Included in Developing an AI Screening System?
Every project includes:
- Data analysis and molecular representation selection (fingerprints, embeddings)
- Building and training surrogate models (QSAR, GNN, Transformer)
- Designing funnel pipeline considering computational resources
- Deployment on GPU infrastructure (Triton Inference Server, ONNX Runtime)
- Integration with databases (PostgreSQL + pgvector for embeddings)
- Documentation, team training, support during operation
Timeline: from 4 weeks for a basic proof-of-concept to 3 months for a full production system. Cost is calculated individually — we'll assess your project upon contact.
We guarantee reproducibility of results and provide model quality certificates. Experience: 30+ projects in drug discovery, 5+ years in AI/ML market. Order development of an AI virtual screening system and get a consultation from our engineers.