In Silico ADME-Tox Prediction and Optimization

In Silico ADME-Tox Prediction and Optimization focuses on computational methods used to evaluate the Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADME-Tox) profiles of drug candidates early in the discovery process. This session highlights tools and models that predict pharmacokinetic behavior, potential toxicity, and safety risks, helping to minimize late-stage failures. Topics include QSAR models, machine learning-based toxicity predictors, and virtual screening filters for drug-likeness. By integrating these in silico approaches, researchers can optimize compound properties more efficiently, reduce animal testing, and accelerate the development of safer, more effective drugs.
• Tools and models for predicting pharmacokinetics
• Early detection of toxicity through simulations
• Integration into early-stage drug development

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