Cut Failures Before They Cost Millions

From Millions of Candidates to Clinical Promise

Virtual screening uses AI to evaluate millions of molecular candidates computationally before physical synthesis and testing, dramatically reducing the cost and time of identifying promising drug leads. AI models trained on biological activity data predict binding affinity, selectivity, and off-target interactions with increasing accuracy, enabling prioritization of the most promising candidates for physical testing from vast chemical libraries.

High-throughput screening (HTS) generates massive experimental datasets that AI uses to identify structure-activity relationships (SAR) and guide lead optimization. Machine learning models trained on HTS data can predict biological activity for novel molecules outside the training set, effectively extrapolating from millions of experimental results to guide synthesis of improved analogs with higher potency, better selectivity, and improved pharmacokinetic properties.

ADMET prediction using AI forecasts how drug candidates will be absorbed, distributed, metabolized, and excreted, and their potential toxicity, before synthesis, enabling medicinal chemists to avoid compounds likely to fail for pharmacokinetic or safety reasons. AI ADMET models have significantly reduced the rate of late-stage clinical failures due to pharmacokinetic issues, which historically caused 40% of drug development failures.

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