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Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
journal contributionposted on 2022-12-02, 03:34 authored by Derek van Tilborg, Alisa Alenicheva, Francesca Grisoni
Machine learning has become a crucial tool in drug discovery and chemistry at large, e.g., to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffspairs of molecules that are highly similar in their structure but exhibit large differences in potencyhave received limited attention for their effect on model performance. Not only are these edge cases informative for molecule discovery and optimization but also models that are well equipped to accurately predict the potency of activity cliffs have increased potential for prospective applications. Our work aims to fill the current knowledge gap on best-practice machine learning methods in the presence of activity cliffs. We benchmarked a total of 24 machine and deep learning approaches on curated bioactivity data from 30 macromolecular targets for their performance on activity cliff compounds. While all methods struggled in the presence of activity cliffs, machine learning approaches based on molecular descriptors outperformed more complex deep learning methods. Our findings highlight large case-by-case differences in performance, advocating for (a) the inclusion of dedicated “activity-cliff-centered” metrics during model development and evaluation and (b) the development of novel algorithms to better predict the properties of activity cliffs. To this end, the methods, metrics, and results of this study have been encapsulated into an open-access benchmarking platform named MoleculeACE (Activity Cliff Estimation, available on GitHub at: https://github.com/molML/MoleculeACE). MoleculeACE is designed to steer the community toward addressing the pressing but overlooked limitation of molecular machine learning models posed by activity cliffs.
received limited attentionmolecular descriptors outperformededge cases informativedeep learning approachescurrent knowledge gapcommunity toward addressing30 macromolecular targetsdedicated “ activitymolecular machine learningactivity cliff estimationactivity cliff compoundshttps :// githubcurated bioactivity datacentered ” metricsexhibit large differencespredict molecular propertiesactivity cliffscase differences24 machinebetter predictaccurately predictwork aimswell equippedprospective applicationsoverlooked limitationnovel algorithmsmolecule discoveryincreased potentialhighly similarhigh accuracyg .drug discoverycrucial toolalso models