Consideration of predicted small-molecule metabolites in computational toxicology

Xenobiotic metabolism has evolved as a key protective system of organisms against potentially harmful chemicals or compounds typically not present in a particular organism. The system's primary purpose is to chemically transform xenobiotics into metabolites that can be excreted via renal or biliary routes. However, in a minority of cases, the metabolites formed are toxic, sometimes even more toxic than the parent compound. Therefore, the consideration of xenobiotic metabolism clearly is of importance to the understanding of the toxicity of a compound. Nevertheless, most of the existing computational approaches for toxicity prediction do not explicitly take metabolism into account and it is currently not known to what extent the consideration of (predicted) metabolites could lead to an improvement of toxicity prediction. In order to study how predictive metabolism could help to enhance toxicity prediction, we explored a number of different strategies to integrate predictions from a state-of-the-art metabolite structure predictor and from modern machine learning approaches for toxicity prediction. We tested the integrated models on five toxicological endpoints and assays, including in vitro and in vivo genotoxicity assays (AMES and MNT), two organ toxicity endpoints (DILI and DICC) and a skin sensitization assay (LLNA). Overall, the improvements in model performance achieved by including metabolism data were minor (up to +0.04 in the F1 scores and up to +0.06 in MCCs). In general, the best performance was obtained by averaging the probability of toxicity predicted for the parent compound and the maximum probability of toxicity predicted for any metabolite. Moreover, including metabolite structures as further input molecules for model training slightly improved the toxicity predictions obtained by this averaging approach. However, the high complexity of the metabolic system and associated uncertainty about the likely metabolites apparently limits the benefit of considering predicted metabolites in toxicity prediction

Characterization of the Chemical Space of Known and Readily Obtainable Natural Products

Natural products remain one of the most productive sources of chemical inspiration for the development of new drugs. The structures of more than 250 000 natural products are available from public databases. At least 10% of these compounds are readily obtainable for experimental testing from commercial vendors and public research institutions. While the physicochemical properties of known natural products have been thoroughly studied and compared to those of drugs and other types of small molecules, the information available on the content, coverage, and relevance of individual virtual and physical natural product libraries is clearly limited. The aim of this study was the development of a detailed understanding of the coverage of chemical space by known and readily obtainable natural products and by individual natural product databases. For this purpose, we compiled comprehensive data sets of known and readily obtainable natural products from 18 virtual databases (including the Dictionary of Natural Products), nine physical libraries, and the Protein Data Bank (PDB). We also developed and employed an algorithm (“SugarBuster”) for the removal of sugars and sugar-like moieties, which are generally not in the focus of interest for drug discovery, from natural products. In addition, we devised a rule-based approach for the automated classification of natural products into natural product classes (alkaloids, steroids, flavonoids, etc.). Among the most important results of this study is the finding that the readily obtainable natural products are highly diverse and populate regions of chemical space that are of high relevance to drug discovery. In some cases, substantial differences in the coverage of natural product classes and chemical space by the individual databases are observed. More than 2000 natural products are identified for which at least one X-ray crystal structure of the compound in complex with a biomacromolecule is available from the PDB

Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data

Machine learning models are widely applied to predict molecular properties or the biological activity of small molecules on a specific protein. Models can be integrated in a conformal prediction (CP) framework which adds a calibration step to estimate the confidence of the predictions. CP models present the advantage of ensuring a predefined error rate under the assumption that test and calibration set are exchangeable. In cases where the test data have drifted away from the descriptor space of the training data, or where assay setups have changed, this assumption might not be fulfilled and the models are not guaranteed to be valid. In this study, the performance of internally valid CP models when applied to either newer time-split data or to external data was evaluated. In detail, temporal data drifts were analysed based on twelve datasets from the ChEMBL database. In addition, discrepancies between models trained on publicly-available data and applied to proprietary data for the liver toxicity and MNT in vivo endpoints were investigated. In most cases, a drastic decrease in the validity of the models was observed when applied to the time-split or external (holdout) test sets. To overcome the decrease in model validity, a strategy for updating the calibration set with data more similar to the holdout set was investigated. Updating the calibration set generally improved the validity, restoring it completely to its expected value in many cases. The restored validity is the first requisite for applying the CP models with confidence. However, the increased validity comes at the cost of a decrease in model efficiency, as more predictions are identified as inconclusive. This study presents a strategy to recalibrate CP models to mitigate the effects of data drifts. Updating the calibration sets without having to retrain the model has proven to be a useful approach to restore the validity of most models

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model ("Skin Doctor CP:Bio") obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds

Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds