Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data

Journal of Cheminformatics

Research

Satvik Singh¹  Ola Spjuth²  Jordi Carreras-Puigvert²  Hongbin Yang³  Andreas Bender³  Maria-Anna Trapotsi³  Srijit Seal³ 

[1] Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge, UK;Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden;Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK;

关键词: Machine learning;    Cell Painting;    Structure;    Toxicity;    Bioactivity;    Applicability domain;   

DOI  :  10.1186/s13321-023-00723-x

 received in 2023-02-05, accepted in 2023-04-20,  发布年份 2023

来源: Springer

【 摘 要 】

The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painting annotations were available). We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC > 0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrated that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and further expanded the applicability domain by better extrapolating to new structural and morphology spaces.Graphical Abstract

【 授权许可】

CC BY   
© The Author(s) 2023

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