A biologically-inspired evaluation of molecular generative machine learning

• URL: http://arxiv.org/abs/2208.09658v1
• Date: Sat, 20 Aug 2022 11:01:10 GMT
• Title: A biologically-inspired evaluation of molecular generative machine learning
• Authors: Elizaveta Vinogradova, Abay Artykbayev, Alisher Amanatay, Mukhamejan Karatayev, Maxim Mametkulov, Albina Li, Anuar Suleimenov, Abylay Salimzhanov, Karina Pats, Rustam Zhumagambetov, Ferdinand Moln\'ar, Vsevolod Peshkov, Siamac Fazli
• Abstract summary: A novel biologically-inspired benchmark for the evaluation of molecular generative models is proposed. We propose a recreation metric, apply drug-target affinity prediction and molecular docking as complementary techniques for the evaluation of generative outputs.
• Score: 17.623886600638716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While generative models have recently become ubiquitous in many scientific areas, less attention has been paid to their evaluation. For molecular generative models, the state-of-the-art examines their output in isolation or in relation to its input. However, their biological and functional properties, such as ligand-target interaction is not being addressed. In this study, a novel biologically-inspired benchmark for the evaluation of molecular generative models is proposed. Specifically, three diverse reference datasets are designed and a set of metrics are introduced which are directly relevant to the drug discovery process. In particular we propose a recreation metric, apply drug-target affinity prediction and molecular docking as complementary techniques for the evaluation of generative outputs. While all three metrics show consistent results across the tested generative models, a more detailed comparison of drug-target affinity binding and molecular docking scores revealed that unimodal predictiors can lead to erroneous conclusions about target binding on a molecular level and a multi-modal approach is thus preferrable. The key advantage of this framework is that it incorporates prior physico-chemical domain knowledge into the benchmarking process by focusing explicitly on ligand-target interactions and thus creating a highly efficient tool not only for evaluating molecular generative outputs in particular, but also for enriching the drug discovery process in general.

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