Matt Segall gave this presentation at the ACS National Spring Meeting 2018 in New Orleans.
Abstract
Compound design requires a combination of knowledge and expertise from different perspectives: understanding of structure-activity relationships (SAR), based on data from previously studied compounds; expertise from diverse fields to define the multi-parameter optimisation (MPO) objectives of a project; and knowledge of synthetic strategies that may be applicable to create the next rounds of compounds for investigation. All of these forms of knowledge can be captured and applied computationally: Machine learning methods can generate quantitative structure-activity relationship (QSAR) models to predict the properties of novel, virtual compounds; MPO methods capture the desired property criteria for a successful compound for a specific project and rigorously prioritise ideas for consideration; and, optimisation strategies can be captured as structural transformations that reflect steps made in previous chemistry projects.
