Extended Data Fig. 3: Comparison of deep learning models for predicting human cell cytotoxicity.

a,b, Precision-recall curves for predictions of HepG2 cytotoxicity, for an ensemble of 10 Chemprop models without RDKit features (a) and the best-performing random forest classifier model based on Morgan fingerprints (b), trained and tested using data from a screen of 39,312 molecules (Fig. 1 of the main text). The black dashed line represents the baseline fraction of active compounds in the training set (8.5%). Blue curves and the 95% confidence interval indicate the variation generated by bootstrapping. AUC, area under the curve. c,d, Precision-recall curves for predictions of HSkMC cytotoxicity, for an ensemble of 10 Chemprop models without RDKit features (c) and the best-performing random forest classifier model based on Morgan fingerprints (d), trained and tested using data from a screen of 39,312 molecules (Fig. 1 of the main text). The black dashed line represents the baseline fraction of active compounds in the training set (3.8%). Blue curves and the 95% confidence interval indicate the variation generated by bootstrapping. e,f, Precision-recall curves for predictions of IMR-90 cytotoxicity, for an ensemble of 10 Chemprop models without RDKit features (e) and the best-performing random forest classifier model based on Morgan fingerprints (f), trained and tested using data from a screen of 39,312 molecules (Fig. 1 of the main text). The black dashed line represents the baseline fraction of active compounds in the training set (8.8%). Blue curves and the 95% confidence interval indicate the variation generated by bootstrapping.