Seminar by Prof. Rocío Mercado on artificial intelligence in biomolecular modeling

OCAMM presents a special seminar by Prof. Rocío Mercado from Chalmers University of Technology, Sweden on “Deep generative models for biomolecular engineering“. This invited seminar will take place at the Department of Chemistry and Materials Science, Aalto University on 13 November 2023 @ 14:00 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus, Kemistintie 1, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi for a great talk on how artificial intelligence and molecular modeling are impacting the fields of biomolecular design and drug discovery.

Title

Deep generative models for biomolecular engineering

Abstract

AI is transforming our approach to molecular engineering. Driven by the goal of accelerating drug development, our aim is to develop AI-driven molecular engineering methods which will enhance our approach to biomolecular discovery, such as drug discovery, drug repurposing, and chemical probe identification. This entails the development of generative and predictive tools that can learn from biochemical data, such as molecular structures, chemical reactions, and biomedical data. While AI can be applied to a range of molecular engineering tasks, one ideal area is de novo molecular design. De novo design is the concept of designing molecules with desired properties from scratch so as to minimize experimental screening, and is poised to allow scientists to more efficiently traverse chemical space in search of optimal molecules, and delegate error-prone decisions to computers via the use of predictive and generative models. In drug development, de novo design methods can aid medicinal chemists in the design and selection of drug candidates, with the added advantage that they can learn from datasets of billions of molecules in minutes and be constantly updated with new data. Deep molecular generative models are a particular approach to de novo design which uses deep neural networks to generate new molecules in silico, and works by proposing atom-by-atom (or fragment-by-fragment) modifications to an initial graph structure to generate compounds predicted to achieve a certain property profile. Such models can be applied to a range of therapeutic modalities.

In this talk, I will discuss the development of deep generative models for various molecular engineering tasks relevant to early-stage drug discovery. These include a model for synthesizability-constrained molecular generation, a reinforcement learning framework for molecular graph optimization, and recent applications from our group to the design of large modalities for targeted protein degradation.

About the speaker

Rocío is a tenure-track assistant professor in the Data Science and AI division at Chalmers since January 2023. She heads the AI Laboratory for Biomolecular Engineering (AIBE) in the Department of Computer Science and Engineering.

Previously, she was a postdoctoral associate in the Coley group at MIT, as well as an industrial postdoc in the Molecular AI team at AstraZeneca. Throughout her postdoctoral career, she worked on the development of deep generative models for small molecule drug discovery. Before AstraZeneca, she was a PhD student in Professor Berend Smit’s molecular simulation group at UC Berkeley and EPFL. She received her PhD in Chemistry from UC Berkeley in August 2018, and her BSc in Chemistry from Caltech in June 2013.