The concept of in silico molecular design goes back decades and has a long history of published approaches using many different algorithms and models. Major challenges involved in de novo molecular design are manifold, including identifying appropriate molecular representations for optimisation, scoring designed molecules against multiple modelled endpoints, and objectively quantifying synthetic feasibility of the designed structures. In particular, the inclusion of important and highly predictive ADME/Tox models, oftentimes not considered into much later than necessary in lead optimisation, can be considered far earlier as part of the design process using appropriate models and sometimes also surrogate properties.<br/><br/>Recently, multiobjective de novo design, more recently referred to as generative chemistry, has had a resurgence of interest. This renaissance has highlighted a step-change in successful applications of such methods. This presentation will review the development of de novo design methods over the years to recent approaches that show great promise, including our recent GuacaMol platform for benchmarking de novo molecular design algorithms. Through this review, improvements in important components of de novo design, including machine learning model predictions and automated synthesis planning, will also be presented.

Incorporating ADME/Tox parameters in optimising multiobjective molecular design

2019 Fall National Meeting

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