Gian Marco Ghiandoni Abstract

Reaction Class Recommendation in De novo Drug Design

Gian Marco Ghiandoni¹, Beining Chen², Dimitar Hristozov³, Michael J. Bodkin³, Valerie J. Gillet¹

The introduction of knowledge-based construction techniques, which combine starting materials and reagents according to a set of rules extracted from collections of known reactions, has reduced the chemical space into a smaller number of potentially accessible structures. For example, reaction vectors, which are representations that encode the topological changes occurring chemical transformations, have been implemented in a structure generation algorithm capable of outputting compound structures along with synthetic references in order to promote their accessibility.[3] In more recent work, reaction vectors have been classified by reaction-type to allow the application of reaction classes to further support the medicinal chemists and limit the chemical space only to regions of interest.[4]

Herein, we build on the reaction classification methods for further knowledge exploitation for de novo design. A series of multi-label machine learning models are trained in order to recommend lists of applicable
reaction classes based upon the features of a given starting material, which can be used to drive the automated generation of product libraries supported by the past experience of medicinal chemists. The best performing model is then validated in a de novo design experiment to highlight the benefits derived from the use of recommendation models. Our conclusion is that recommendation models lead to a higher synthetic accessibility of products and a more efficient management of computational resources.