Accelerating virtual screening by 61% with expert-driven computational biology

The biotech firm set out to enhance their in-silico discovery engine with next-generation computational models. The initiative required not only accelerating ultra-large library screening, but also improving target accuracy, synthetic accessibility, and ADMET prediction. The ultimate goal: shorten the discovery-to-clinic pipeline while cutting costs and attrition.

• Integrate genomic and structural data for novel target discovery
• Improve hit quality and reduce false positives in predictions
• Enable faster iteration on compound optimization cycles
• Advance preclinical candidates with stronger validation confidence

Despite heavy infrastructure investment, existing virtual screening workflows struggled with both speed and precision. Competing AI platforms outperformed them, eroding competitive edge.

• Screening 10B compound libraries took 8 months via conventional methods
• AlphaFold-derived structures achieved only 67% accuracy at binding sites
• Multi-omics integration captured 34% of relevant disease pathways
• Prior deep learning models required 50K+ actives to perform acceptably
• Computational hits validated experimentally only 29% of the time
• Competitors achieved 5x faster iteration with newer architectures

CleverX mobilized a specialized cross-functional team of computational chemists, bioinformaticians, ML engineers, and structural biologists. Together they redesigned pipelines to handle extreme data loads while improving prediction reliability.

Expert recruitment:

• 8 PhD computational chemists in molecular dynamics/quantum mechanics
• 6 bioinformaticians for pathway analysis and target deconvolution
• 3 ML engineers with chemistry-specific architecture experience
• 2 structural biologists focused on protein-protein interactions
• Collective record: 120+ publications and experience at Atomwise, Recursion, Schrödinger

Technical framework:

• Geometric deep learning on 3D molecular structures
• Transformer-based SMILES generation for novel molecules
• Federated learning combining proprietary + public datasets
• Physics-informed neural nets with quantum mechanical constraints

Quality protocols:

• Orthogonal validation using multiple prediction methods
• Retrospective testing on 50 historical programs
• Confidence scoring for uncertain predictions
• Interpretable AI outputs explaining structural rationales

A phased rollout tackled infrastructure first, then modeling, and finally validation—shortening feedback loops and cutting screening timelines by more than half.

Weeks 1-2: Data infrastructure and computational pipeline setup

• Integrated 15 public databases with 100M data points
• Built distributed computing handling 1TB/day throughput
• Deployed 200 A100 GPUs for large-scale training
• Automated pipeline from target sequence to lead molecules

Weeks 3-6: Model development and training

• Trained protein-ligand models on 2M crystal structures
• Developed generative models creating 100K molecules/day
• Built ADMET prediction ensemble with 0.89 AUC
• Created synthetic route predictor at 82% accuracy

Weeks 7-10: Virtual screening campaign

• Screened 5B compounds in 2 weeks (vs 8 months prior)
• Identified 450 novel scaffolds across 6 targets
• Predicted 23K drug-target interactions for repositioning
• Generated 50K optimized analogs

Weeks 11-14: Experimental validation and refinement

• Wet-lab validated 500 computational hits
• Improved hit rate to 47% (vs 8% baseline)
• Iteratively retrained models with experimental feedback
• Advanced 12 candidates into preclinical testing

Computational efficiency:

The platform achieved breakthrough performance in speed and scalability.

• Screening time cut 61% (8 months → 3 months)
• Cost per screen down 72% through optimization
• Lead optimization cycles shortened 16 → 6 weeks
• Scaffold hopping success rate improved 3.4x

Discovery performance:

Faster models translated into dramatically higher discovery productivity.

• Hit rate from virtual screening jumped 8% → 47%
• 23 first-in-class compounds discovered
• ADMET prediction accuracy improved to 89%
• Experimental validation needs reduced 65%

Business impact:

The improvements yielded direct financial and strategic returns.

• Saved $6.2M/year in HTS and synthesis costs
• Advanced 3 programs to IND 18 months faster
• Raised $85M Series C funding off platform success
• Licensed platform to 2 pharma partners for $12M upfront

Strategic advantages:

The system created durable competitive differentiation.

• Built proprietary database with 10B annotated compounds
• Cloud-native platform scaled to exascale computing
• 3 novel graph neural networks patented
• Published in Nature Computational Science

The company's computational platform was recognized with Bio-IT World Innovative Practices Award for advancing in-silico drug discovery.