AI Virtual Molecular Screening System

Developing AI System for Virtual Molecular Screening

Virtual screening — computer-based candidate selection from large molecular libraries before physical synthesis and testing. AI transforms screening billions of molecules from impossible task to routine operation.

Virtual Screening Methods

Ligand-Based Screening (LBVS)

Uses information about known active molecules. If we have set of active molecules against target — search 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 molecules with same spatial arrangement
• QSAR (Quantitative Structure-Activity Relationship): ML model predicts pIC50 from structural features

Structure-Based Screening (SBVS)

Uses 3D structure of target protein. Molecules docked into active site.

Classical SBVS bottleneck: docking 1 molecule takes seconds → 1 billion molecules = 30 years CPU. AI solutions:

• Surrogate ML models: fast ML scoring (milliseconds) replaces docking as 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

Ultra-Large Library Screening

Enamine REAL Space: 36 billion synthetically accessible molecules. How to screen efficiently?

Molecular Embeddings

Training encoder (Transformer or GNN) for compact vector representation of molecules. Searching nearest neighbors in embedding space in milliseconds. FAISS (Facebook AI Similarity Search) for indexing billions of vectors.

Generative Screening (Make-on-Demand)

Instead of screening ready library — generate new molecules with needed properties in space of synthetically accessible structures. Reinvent, SAFE (IUPAC), Synthetically Accessible Drug Space.

Hierarchical Narrowing (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 next level's capacity.

Active Learning for Screening

Traditional VS: random selection for testing. Active Learning: ML model selects which molecules most informative for next experiment iteration.

Cycle:

1. Initial dataset (1000 molecules with measured activity)
2. Training surrogate model
3. Acquisition function selects next 100 molecules (Expected Improvement, UCB)
4. Synthesis + test
5. Repeat

Reduction in required syntheses: 5–20x for finding active hits compared to random screening.

Screening Efficiency Metrics

Goal: EF@1% > 50 (in top 1% molecules 50 times more active 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 analysis depth.