Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data.

Mitochondrial toxicity is a significant concern in the drug discovery process, as compounds that disrupt the function of these organelles can lead to serious side effects, including liver injury and cardiotoxicity. Different in vitro assays exist to detect mitochondrial toxicity at varying mechanistic levels: disruption of the respiratory chain, disruption of the membrane potential, or general mitochondrial dysfunction. In parallel, whole cell imaging assays like Cell Painting provide a phenotypic overview of the cellular system upon treatment and enable the assessment of mitochondrial health from cell profiling features. In this study, we aim to establish machine learning models for the prediction of mitochondrial toxicity, making the best use of the available data. For this purpose, we first derived highly curated datasets of mitochondrial toxicity, including subsets for different mechanisms of action. Due to the limited amount of labeled data often associated with toxicological endpoints, we investigated the potential of using morphological features from a large Cell Painting screen to label additional compounds and enrich our dataset. Our results suggest that models incorporating morphological profiles perform better in predicting mitochondrial toxicity than those trained on chemical structures alone (up to +0.08 and +0.09 mean MCC in random and cluster cross-validation, respectively). Toxicity labels derived from Cell Painting images improved the predictions on an external test set up to +0.08 MCC. However, we also found that further research is needed to improve the reliability of Cell Painting image labeling. Overall, our study provides insights into the importance of considering different mechanisms of action when predicting a complex endpoint like mitochondrial disruption as well as into the challenges and opportunities of using Cell Painting data for toxicity prediction.

In Silico Models to Predict the Perturbation of Molecular Initiating Events Related to Thyroid Hormone Homeostasis.

Disturbance of the thyroid hormone homeostasis has been associated with adverse health effects such as goiters and impaired mental development in humans and thyroid tumors in rats. In vitro and in silico methods for predicting the effects of small molecules on thyroid hormone homeostasis are currently being explored as alternatives to animal experiments, but are still in an early stage of development. The aim of this work was the development of a battery of in silico models for a set of targets involved in molecular initiating events of thyroid hormone homeostasis: deiodinases 1, 2, and 3, thyroid peroxidase (TPO), thyroid hormone receptor (TR), sodium/iodide symporter, thyrotropin-releasing hormone receptor, and thyroid-stimulating hormone receptor. The training data sets were compiled from the ToxCast database and related scientific literature. Classical statistical approaches as well as several machine learning methods (including random forest, support vector machine, and neural networks) were explored in combination with three data balancing techniques. The models were trained on molecular descriptors and fingerprints and evaluated on holdout data. Furthermore, multi-task neural networks combining several end points were investigated as a possible way to improve the performance of models for which the experimental data available for model training are limited. Classifiers for TPO and TR performed particularly well, with F1 scores of 0.83 and 0.81 on the holdout data set, respectively. Models for the other studied targets yielded F1 scores of up to 0.77. An in-depth analysis of the reliability of predictions was performed for the most relevant models. All data sets used in this work for model development and validation are available in the Supporting Information.

ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities.

Computational methods such as machine learning approaches have a strong track record of success in predicting the outcomes of in vitro assays. In contrast, their ability to predict in vivo endpoints is more limited due to the high number of parameters and processes that may influence the outcome. Recent studies have shown that the combination of chemical and biological data can yield better models for in vivo endpoints. The ChemBioSim approach presented in this work aims to enhance the performance of conformal prediction models for in vivo endpoints by combining chemical information with (predicted) bioactivity assay outcomes. Three in vivo toxicological endpoints, capturing genotoxic (MNT), hepatic (DILI), and cardiological (DICC) issues, were selected for this study due to their high relevance for the registration and authorization of new compounds. Since the sparsity of available biological assay data is challenging for predictive modeling, predicted bioactivity descriptors were introduced instead. Thus, a machine learning model for each of the 373 collected biological assays was trained and applied on the compounds of the in vivo toxicity data sets. Besides the chemical descriptors (molecular fingerprints and physicochemical properties), these predicted bioactivities served as descriptors for the models of the three in vivo endpoints. For this study, a workflow based on a conformal prediction framework (a method for confidence estimation) built on random forest models was developed. Furthermore, the most relevant chemical and bioactivity descriptors for each in vivo endpoint were preselected with lasso models. The incorporation of bioactivity descriptors increased the mean F1 scores of the MNT model from 0.61 to 0.70 and for the DICC model from 0.72 to 0.82 while the mean efficiencies increased by roughly 0.10 for both endpoints. In contrast, for the DILI endpoint, no significant improvement in model performance was observed. Besides pure performance improvements, an analysis of the most important bioactivity features allowed detection of novel and less intuitive relationships between the predicted biological assay outcomes used as descriptors and the in vivo endpoints. This study presents how the prediction of in vivo toxicity endpoints can be improved by the incorporation of biological information-which is not necessarily captured by chemical descriptors-in an automated workflow without the need for adding experimental workload for the generation of bioactivity descriptors as predicted outcomes of bioactivity assays were utilized. All bioactivity CP models for deriving the predicted bioactivities, as well as the in vivo toxicity CP models, can be freely downloaded from https://doi.org/10.5281/zenodo.4761225.

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.