Orazio Nicolotti

Target‐Engagement Prediction From Molecular Structure, In Vitro and Animal Data: A Tutorial for Early Assessment of Clinical Drug Exposure and Therapeutic/Adverse Potential Using the ANDROMEDA and PLATO Software on the InSilicoTrials Platform

The target-engagement predictor, implemented within the InSilicoTrials cloud-based, AI-powered platform for model-informed drug discovery and development, enables early exploration of time-dependent pharmacological responses. Predictions can be generated using inputs ranging from...

Language of Toxicity: An eXplainable Artificial Intelligence Approach

Abstract Toxicity prediction in small molecules represents a fundamental challenge in drug development and chemical safety assessment. Traditional approaches heavily rely on predefined molecular descriptors or fingerprints, potentially limiting the ability to capture complex and...

Sustained seizure reduction, treatment simplification, and high retention with cenobamate: A 12‐month real‐world study in refractory and ultra‐refractory focal epilepsy

Abstract Objective To assess the 12‐month clinical impact of cenobamate (CNB) in adults with drug‐resistant focal epilepsy in clinical practice, exploring whether outcomes differed according to prior antiseizure medication (ASM) exposure. Methods This single‐center, retrospective...