Optimizing candidates, Eliminating Avoidable Failure

Predicts hepatotoxicity, cardiotoxicity, neurotoxicity, genotoxicity, mitochondrial disruption, and hERG liabilities across candidate molecules. Generative adversarial networks iteratively refine molecular structures to remove toxicity-prone functional groups, while cross-referencing large-scale toxicology datasets to prevent known adverse effects prior to preclinical testing.

Models key pharmacokinetic and pharmacodynamic properties, including absorption metrics such as solubility, bioavailability, and membrane permeability; distribution features including blood–brain barrier penetration and plasma protein binding; metabolic stability and CYP450 interactions; renal and hepatic clearance; and quantitative toxicity endpoints such as LD50, mutagenicity, and carcinogenicity. All predictions are powered by in silico models trained on curated datasets of FDA-approved and clinically validated compounds.

Applies multi-objective reinforcement learning to suggest molecular modifications that improve efficacy, selectivity, and bioavailability while minimizing side effects. Evaluates potential drug–drug interactions and off-target effects, ensuring that prioritized candidates maximize therapeutic potential while minimizing clinical risk.