Abstract
Harnessing artificial intelligence for de novo drug design
Artificial intelligence (AI) is fueling computer-aided drug and molecule discovery. Chemical language models (CLMs) are one of the most recent additions to the medicinal chemist’s toolkit for AI-driven molecule design. CLMs can be used to generate novel molecules in the form of strings (e.g., SMILES or amino-acid sequences) without relying on human-engineered assembly rules. Thanks to such a ‘rule-free’ character, CLMs allow navigating the chemical space and generating focused chemical libraries. In multiple instances, CLMs have shown able to learn “grammar” rules for molecule construction, and to implicitly capture “semantic” features, such as physicochemical properties, bioactivity, and chemical synthesizability. This talk will illustrate some successful applications of CLMs to design novel bioactive compounds from scratch, e.g., natural-product-inspired modulators of nuclear receptors,4d and in combination with automated synthesis. Moreover, the talk will provide a personal perspective on current limitations and future opportunities for AI in medicinal and organic chemistry, to accelerate molecule discovery and chemical space exploration.
Biography
Francesca Grisoni is a Tenure Track Assistant Professor at the Eindhoven University of Technology (Chemical Biology, Dept. Biomedical Engineering), where she leads the Molecular Machine Learning team. After receiving her PhD in 2016 at the University of Milano-Bicocca, with a dissertation on machine learning for (eco)toxicology, Francesca worked as a data scientist and as a biostatistical consultant for the pharmaceutical industry. Later, she joined the University of Milano-Bicocca (in 2017) and the ETH Zurich (in 2019) as a postdoctoral researcher, working on machine learning for drug discovery and molecular property prediction. Her current research focuses on developing novel chemistry-centered AI methods to augment human intelligence in drug discovery, at the interface between computation and prospective application.
