The manner by which we approach molecular and materials discovery problems increasingly makes use of computational algorithms to support our decision-making. As discovery is almost inherently an iterative process, one of the clearest opportunities to apply computational decision-making during design cycles is in hypothesis generation: proposing candidate solutions that we believe exhibit the right set of properties. In this talk, we will discuss some approaches that can be taken to accelerate virtual screening pipelines as well as new <i>de novo</i> design strategies to propose actionable designs. That is, models that take into consideration our experimental capabilities, which we believe will be an essential component of future autonomous laboratories.

Computational hypothesis generation and experimental design for autonomous molecular discovery

Where thousands of chemistry professionals meet to share ideas and advance scientific and technical knowledge. Spring 2023 abstracts, posters, and presentations will launch at the end of March.

ACS Spring 2023

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.