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Workshop: Machine Learning for Drug Discovery (MLDD)

An evaluation framework for the objective functions of de novo drug design benchmarks

Austin Tripp · Wenlin Chen · José Miguel Hernández Lobato

Keywords: [ molecular optimization ] [ benchmark ]


De novo drug design has recently received increasing attention from the machine learning community. It is important that the field is aware of the actual goals and challenges of drug design and the roles that de novo molecule design algorithms could play in accelerating the process, so that algorithms can be evaluated in a way that reflects how they would be applied in real drug design scenarios. In this paper, we propose a framework for critically assessing the merits of benchmarks, and argue that most of the existing de novo drug design benchmark functionsare either highly unrealistic or depend upon a surrogate model whose performance is not well characterized. In order for the field to achieve its long-term goals, we recommend that poor benchmarks (especially logP and QED) be deprecated in favour of better benchmarks. We hope that our proposed framework can play a part in developing new de novo drug design benchmarks that are more realistic and ideally incorporate the intrinsic goals of drug design.

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