Out now in International Biopharmaceutical Industry, Optibrium’s CEO, Dr Matt Segall introduces the concept of augmented intelligence. He explains how to use dynamic learning to generate better compounds for your drug discovery programs.

Matt talks through practical examples of augmented intelligence, demonstrating how combining the experience of skilled medicinal chemists with powerful machine learning (ML) generative chemistry algorithms enables humans and machines to learn from and reinforce one another. This overcomes their individual weaknesses and leads to better drug discovery outcomes.

Introduction

The ability of computers to produce new compound structures using ‘generative chemistry’ algorithms is a hot topic in drug discovery. The field has recently been reinvigorated by new machine learning (ML) algorithms that learn what a drug-like molecule ‘looks like’ and subsequently generate large numbers of chemically meaningful structures.

A key advantage of ML algorithms is that they can explore the chemical space around a lead or series, and are able to generate vastly more compounds than either an individual or a team of experts. In addition, these algorithms can be combined with predictive models of target activities and other compound properties, to identify high-quality chemical suggestions.

ML methods have the ability to learn from much more data than any human expert, enabling the identification of complex patterns and guiding future compound optimisation. The resulting generative chemistry systems can also be applied objectively, thereby challenging human biases to enable a more rigorous exploration of optimisation strategies [1].

[1] M. Segall, T. Mansley, P. Hunt and E. Champness, “Capturing and Applying Knowledge to Guide Compound Optimisation,” Drug Discov. Today, vol. 25, no. 5, pp. 1074-1080, 2019.

About the author

Matt Segall, PhD

CEO, Optibrium

Profile

The image shows Optibrium CEO Matthew Segall

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