Agenda

Speaker: Silvia Multari, Ca' Foscari University of Venice

Abstract:
Drug design is a central component of the drug development process, aimed at the identification and optimization of bioactive molecules capable of selectively modulating biological targets. The exploration of chemical space, which comprises an astronomically large number of possible compounds, represents a major challenge that cannot be addressed through experimental screening alone. Computational approaches have therefore become essential tools to guide molecular discovery and reduce time and cost in early-stage drug development. In recent years, Machine Learning and Computational Intelligence have further expanded the capabilities of computational drug design, enabling data-driven prediction, structure-guided optimization, and automated exploration of chemical space. These methods allow the integration of chemical, biological, and pharmacokinetic constraints already at the design stage, improving the quality of candidate molecules prior to preclinical validation. This seminar will present an overview of drug design in the context of modern drug development, with a focus on computational strategies empowered by artificial intelligence.

Bio sketch:
Silvia Multari is a PhD candidate in Science and Technology of Bio and Nano Materials at the Ca’ Foscari University of Venice, Department of Molecular Sciences and Nanosystems. She holds a bachelor’s degree in Pharmaceutical Biotechnology obtained at the University of Milan, and she is now specialising in computational applications to drug discovery, with a focus on the study of molecular interactions, combining physics-based and AI-driven approaches to investigate peptide and small-molecule binding. She was a visiting student at the Technical University of Eindhoven (Eindhoven, the Netherlands) in the Molecular Machine Learning group led by professor Francesca Grisoni, and then at the Kyoto Institute of Technology (Kyoto, Japan) under the supervision of professor Giuseppe Pezzotti. Her main expertise lies in the application of molecular dynamics simulations, molecular docking and machine learning.