Chemical Engineering Seminar
Connor W. Coley is an Assistant Professor at MIT in the Department of Chemical Engineering and the Department of Electrical Engineering and Computer Science. He received his B.S. and Ph.D. in Chemical Engineering from Caltech and MIT, respectively, and did his postdoctoral training at the Broad Institute. His research group at MIT develops new methods at the intersection of data science, chemistry, and laboratory automation to streamline discovery in the chemical sciences with an emphasis on therapeutic discovery. Key research areas in the group include the design of new neural models for representation learning on molecules, data-driven synthesis planning, in silico strategies for predicting the outcomes of organic reactions, model-guided Bayesian optimization, and de novo molecular generation. Connor is a recipient of C&EN’s “Talented Twelve” award, Forbes Magazine’s “30 Under 30” for Healthcare, the NSF CAREER award, and the Bayer Early Excellence in Science Award. Outside of MIT, Connor serves as an advisor to both early- and late-stage companies including Entos, Revela, Galixir, Kebotix, Anagenex, and Dow.
The identification and synthesis of molecules that exhibit a desired function is an essential part of addressing contemporary problems in science and technology. Small molecules are the predominant solution to challenges in the development of medicines, chemical probes, specialty polymers, and organocatalysts, among others. The typical discovery paradigm is an iterative process of designing candidate compounds, synthesizing those compounds, and testing their performance experimentally, where each repeat of this cycle can require weeks or months. There are a variety of techniques used for prioritizing experiments based on the predicted property profiles of molecules. In preclinical drug discovery applications, ligand-based methods may rely on similarity calculations or quantitative structure-activity/property relationship (QSAR/QSPR) models fit to experimental data of bioactivity, solubility, toxicity, etc. Structure-based methods may employ a computational "funnel" approach to score molecules, first using coarse techniques like docking and progressing to higher-fidelity methods like molecular dynamics simulations with explicit solvent.
In this talk, we will focus on a primary consideration of molecular design workflows: the chemical space that comprises the search space for a molecular screening/optimization campaign. That is, the manner in which the search is constrained to a finite library of molecules or, following an increasingly popular trend, the manner in which the search navigates a virtually infinite space of molecules. An orthogonal consideration is whether the search space is constrained or unconstrained in terms of synthesizability (or commercial availability), which impacts the ease of experimental validation. We will talk about how models to predict chemical reactivity inform our ability to define and navigate these spaces, and how literature data can enable the development of models for predictive chemistry.