Chemical space is large, to the point of precluding its explicit enumeration. Thus, it represents a so-called intensionally designed space. Search strategies for intensionally designed spaces are a current area of interest in machine learning. In the context of a drug design problem, we have investigated the application of a Metropolis–Hastings sampling scheme, where the acceptance probability is given by a probabilistic surrogate of the target property, modelled with a max entropy conditional model. We apply the approach to a lead development search for an antagonist of an alpha-v integrin, using a molecular docking score as the optimisation function.

Active search for computer-aided drug design

255th National Meeting (2018)

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