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

Published version
Peer-reviewed

Repository DOI


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Authors

Seal, Srijit 
Yang, Hongbin 
Trapotsi, Maria-Anna 
Singh, Satvik 
Carreras-Puigvert, Jordi 

Abstract

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.

Description

Funder: Uppsala University

Keywords

Applicability domain, Bioactivity, Cell Painting, Machine learning, Structure, Toxicity

Journal Title

J Cheminform

Conference Name

Journal ISSN

1758-2946
1758-2946

Volume Title

Publisher

Springer Science and Business Media LLC
Sponsorship
Engineering and Physical Sciences Research Council (EP/P020259/1)
Swedish Research Council (grants 2020-03731 and 2020-01865), and FORMAS (grant 2018-00924). Cambridge Centre for Data Driven Discovery and Accelerate Programme for Scientific Discovery under the project title “Theoretical, Scientific, and Philosophical Perspectives on Biological Under-standing in the Age of Artificial Intelligence”, made possible by a donation from Schmidt Futures.
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