Eliminating Toxicity, Optimizing Drug Candidates

Analyzes molecular structures to predict hepatotoxicity, cardiotoxicity, neurotoxicity, and genotoxicity risks.

Uses generative adversarial networks (GANs) to refine molecular structures and eliminate toxicity-prone functional groups.

Compares candidates against a vast toxicology database to prevent known adverse effects before preclinical testing.

Predicts key pharmacokinetic properties, including:

Suggests molecular modifications to enhance efficacy while reducing side effects.

Utilizes multi-objective reinforcement learning to balance potency, safety, and bioavailability.

Predicts drug-drug interactions (DDIs) and off-target effects to mitigate adverse reactions.

Simulates real-time drug interactions at the atomic level, predicting stability and efficacy.

Detects molecular binding efficiency, conformational changes, and metabolic degradation.

Enhances precision in predicting PK/PD (pharmacokinetics/pharmacodynamics) profiles.