Small molecule drug discovery involves a complex multi-parameter optimisation process with cycles of design, make and test to establish a desired compound profile. Within this context, machine learning methods, experimental design and de-novo structure generation have all found a place to facilitate and accelerate lead optimisation. However, they have tended to be used in a reactive manner, to address problems posed by program teams rather than as a continual and proactive process. In this presentation we will describe how data-driven cheminformatics based AI methods has the potential to automate parts of the lead optimisation process which historically has been a very time consuming task. This strategy adds the ability to explore and exploit multiple paths within chemical space and suggest structural modifications that will gain better understanding of the SAR problem at hand. In addition, by continually improving computational models we can extrapolate to new regions of chemical space and suggest novel compounds. The implications of automation for the human-machine interface will be explored and illustrated with examples from BRADSHAW, GSK’s experimental automated design environment.

Explore, exploit, and extrapolate: How AI-driven SAR navigation facilitates lead optimisation in drug discovery

2019 Fall National Meeting

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