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.