AI-Driven Drug Discovery
Mapping the landscape of explainable deep-learning platforms to deliver robust, interpretable, and synthesis-aware lead lists for IND-bound programmes.
Client
Global Pharmaceutical Company
Objective
Find Interpretable & Robust AI Platforms
Timeline
10-Week Sprint
Key Focus
Explainability & Synthetic Feasibility
The Challenge: Bottlenecks Throttling AI's Promise
Deep-learning engines promise to compress the 18-24 month hit-identification stage into months. Yet, progress is throttled by four intertwined bottlenecks.
Data Sparsity & Noise
Historic assay libraries lack sufficient "negative" examples and contain variability that confuses models.
Proprietary Silos
Crucial bioactivity data are locked in disconnected corporate vaults, preventing large, diverse training sets.
Model Interpretability
"Black-box" neural nets give predictions with little chemical rationale, leaving chemists wary of AI.
Synthetic Feasibility
Many high-scoring in-silico hits prove impractical or costly to synthesise at scale in the wet lab.
The Outcomes: A Platform for Trusted Discovery
Our work identified platforms and strategies that directly address the core bottlenecks, culminating in a powerful ROI model.
50%
Reduction in Lead-ID Timeline
Projected vs. legacy docking and High-Throughput Screening (HTS) workflows.
4x
Improvement in Hit-to-Lead Conversion
Relative to legacy methods, feeding a stronger, AI-vetted pipeline into pre-clinical.
Additional Deliverables:
10 priority recommendations including graph-neural-network suites with built-in retrosynthesis filters, and a draft data-licence template enabling secure cross-company sharing of negative assay data to boost model robustness.
Strategic Impact: A Dual-Track Pilot
The pharma client approved a dual-track pilot based on our recommendations, positioning them to shorten discovery cycles and increase chemist acceptance of AI-vetted hits.
Track 1: Federated Learning
Deployment of a secure, multi-party federated learning platform for the client's oncology portfolio, enabling model training without exposing proprietary data.
Track 2: Explainable AI Engine
Integration of an explainable GNN-retrosynthesis engine into the core medicinal-chemistry workflow to quicken triage of non-synthesizable hits.