Accelerated molecular design and synthesis for drug discovery
Connor Coley, Massachusetts Institute of Technology
The typical molecular discovery paradigm is an iterative process of designing candidate compounds, synthesizing those compounds, and testing their performance, where each repeat of this cycle can require weeks or months, requires extensive manual effort, and relies on expert intuition. Computational tools from machine learning to laboratory automation have already started to streamline this process and promise to transition molecular discovery from intuition-driven to information-driven. This talk will provide an overview of our efforts to develop "predictive chemistry" tools to accelerate the planning and execution of chemical syntheses, as well as deep generative models that learn to propose new molecular structures that can be validated in the lab.
References:
https://www.science.org/doi/10.1126/science.aax1566 [science.org]