Drug Discovery

AI accelerates drug discovery by optimizing candidate identification and reducing development time

Business impact

• Time to market — AI shortens drug discovery cycles, enabling faster candidate identification and approval
• R&D efficiency — Automation and AI models increase throughput and reduce manual experimental workload
• Drug candidate success rate — Improved prediction accuracy leads to higher quality candidates entering clinical trials
• Cost reduction — AI reduces expensive physical experiments and resource consumption in discovery phases

Data requirements

• Biological assay data (Numeric) — Used to train AI models on drug-target interactions and efficacy
• Chemical structure databases (Structured) — Provide molecular information for virtual screening and generative design
• Genomic and proteomic data (Text) — Enable target identification and biomarker discovery through multi-omics analysis
• Imaging data (e.g., microscopy) (Image) — Supports phenotypic screening and cellular response modeling
• High-throughput screening outputs (Numeric) — Feed into AI for hit identification and lead optimization

AI methods and techniques

• Predictive AI — Predicts drug-target interactions, efficacy, and toxicity to prioritize candidates
• Generative AI — Designs novel molecules and optimizes chemical properties for drug candidates
• Agentic AI — Automates experimental workflows and decision-making in self-driving labs
• Symbolic AI — Incorporates domain knowledge and rules for interpretable drug design

AI models and model families

GPT-4o, Claude, Llama, BioNeMo, RFdiffusion, AlphaFold3

Industries

Real-world evidence

33 documented case studies on record.

Companies using this: Absci, Amgen, Atomwise, Cortical Labs, Curve Therapeutics, Deloitte, Ernst Young, Exscientia, Generate Biomedicines, Genetics, Global Health Drug Discovery Institute GHDDI, Google, IBM, Innophore, Insilico Medicine and 18 more.