AI-Accelerated Drug Candidate Screening

A biotech startup was spending $2.8M per compound in early-stage screening, with 94% of candidates failing in pre-clinical phases. Their pipeline for a rare cardiovascular target had stalled with only 8 viable candidates after 3 years.

We built a molecular property prediction system using graph neural networks trained on 14M+ compound-assay pairs from public and proprietary datasets. The system predicts ADMET properties, target binding affinity, and toxicity flags, reducing wet lab screening from 2,000 candidates to the top 40 most promising.