אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Deep learning has revolutionized drug development by enhancing various stages of the process, yet traditional optimization goals often lack the sophistication required for real-world applications. This work tackles more complex and nuanced problems beyond conventional property enhancement, focusing on optimizing under patentability constraints and translating preclinical success in animals to human clinical trials. In addressing patentability, we introduce a patent loss mechanism and the Molecular Optimization Model with Patentability Constraint (MOMP) to ensure generated molecules are novel and sufficiently different from existing patents, thereby enhancing their potential for successful patenting. Our approach navigates the intricate landscape of patentability, diverging from typical property optimization methods that rely on straightforward classifier definitions. Additionally, we address the challenge of translating drug efficacy from animals to humans, introducing the Biological Complexity Curriculum Learning for Molecular Activity Prediction in Humans (BioMolX). Utilizing state-of-the-art Graph Neural Networks and curriculum learning, BioMolX progressively trains on animal data of increasing physiological complexity to enhance predictive accuracy for human tissues. Building on this, our Rat2Human model optimizes molecules effective in rats to ensure similar efficacy in humans, employing a transformer architecture to learn transformations at both atomic and molecular levels across multiple tissues. These innovative methodologies demonstrate a holistic approach to drug development, tackling complex challenges to generate novel compounds and accelerate the delivery of effective treatments to patients.