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Wu, L.* ; Huang, R.* ; Tetko, I.V. ; Xia, Z. ; Xu, J.* ; Tong, W.*

Trade off predictivity and explainability for machine-learning powered predictive toxicology: An in-depth investigation with Tox21 data sets.

Chem. Res. Toxicol. 34, 541-549 (2021)
Postprint DOI PMC
Open Access Green
Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess algorithm and feature influences on model performance in chemical toxicity research. We conducted over 5000 models for a Tox21 bioassay data set of 65 assays and ∼7600 compounds. Seven molecular representations as features and 12 modeling approaches varying in complexity and explainability were employed to systematically investigate the impact of various factors on model performance and explainability. We demonstrated that end points dictated a model's performance, regardless of the chosen modeling approach including deep learning and chemical features. Overall, more complex models such as (LS-)SVM and Random Forest performed marginally better than simpler models such as linear regression and KNN in the presented Tox21 data analysis. Since a simpler model with acceptable performance often also is easy to interpret for the Tox21 data set, it clearly was the preferred choice due to its better explainability. Given that each data set had its own error structure both for dependent and independent variables, we strongly recommend that it is important to conduct a systematic study with a broad range of model complexity and feature explainability to identify model balancing its predictivity and explainability.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 0893-228X
e-ISSN 1520-5010
Zeitschrift Chemical Research in Toxicology
Quellenangaben Band: 34, Heft: 2, Seiten: 541-549 Artikelnummer: , Supplement: ,
Verlag American Chemical Society (ACS)
Verlagsort 1155 16th St, Nw, Washington, Dc 20036 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)
Förderungen CONCERT REACH project
China Scholarship Council (CSC)
Intramural/Extramural research program of the NCATS, NIH