Structural alerts are a simple and easy way to identify toxic compounds being widely used in environmental toxicology research and drug discovery. With the emergence of big data techniques in recent years and their applications in chemistry and toxicology, computational approaches have become a promising method to identify structural alerts. In this Review, we describe the recent progress in computational methods for identification of structural alerts and their applications in toxicology. Two major computational approaches, namely frequency analysis and interpretable machine learning models, are reviewed. Recent studies have shown that both approaches are superior to expert systems with respect to predictive capability. Methodologies for defining the applicability domain of such approaches are also reviewed, with their importance stemming from their ability to not only improve the predictive performance of structural alert models but also ensure the confidence of a prediction. In addition to toxicity prediction, structural alerts could be also used to explain quantitative structure−activity relationship models and guide lead optimization in drug discovery. Nevertheless, there are still some challenges to be solved, such as how to address the co-existence of several structural alerts in one molecule, how to directly compare computationally derived structural alerts with expert systems, and how to explore new mechanisms of toxicity.■ CONTENTS
The Janus kinase (JAK) family plays a pivotal role in most cytokine-mediated inflammatory and autoimmune responses via JAK/STAT signaling, and administration of JAK inhibitors is a promising therapeutic strategy for several diseases including COVID-19. However, to screen and design selective JAK inhibitors is a daunting task due to the extremely high homology among four JAK isoforms. In this study, we aimed to simultaneously predict pIC50 values of compounds for all JAK subtypes by constructing an interpretable GNN multitask regression model. The final model performance was positive, with R2 values of 0.96, 0.79 and 0.78 on the training, validation and test sets, respectively. Meanwhile, we calculated and visualized atom weights, followed by the rank sum tests and local mean comparisons to obtain key atoms and substructures that could be fine-tuned to design selective JAK inhibitors. Several successful case studies have demonstrated that our approach is feasible and our model could learn the interactions between proteins and small molecules well, which could provide practitioners with a novel way to discover and design JAK inhibitors with selectivity.
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The prediction and optimization of pharmacokinetic properties are essential in lead optimization. Traditional strategies mainly depend on the empirical chemical rules from medicinal chemists. However, with the rising amount of data, it is getting more difficult to manually extract useful medicinal chemistry knowledge. To this end, we introduced IDL-PPBopt, a computational strategy for predicting and optimizing the plasma protein binding (PPB) property based on an interpretable deep learning method. At first, a curated PPB data set was used to construct an interpretable deep learning model, which showed excellent predictive performance with a root mean squared error of 0.112 for the entire test set. Then, we designed a detection protocol based on the model and Wilcoxon test to identify the PPB-related substructures (named privileged substructures, PSubs) for each molecule. In total, 22 general privileged substructures (GPSubs) were identified, which shared some common features such as nitrogen-containing groups, diamines with two carbon units, and azetidine. Furthermore, a series of second-level chemical rules for each GPSub were derived through a statistical test and then summarized into substructure pairs. We demonstrated that these substructure pairs were equally applicable outside the training set and accordingly customized the structural modification schemes for each GPSub, which provided alternatives for the optimization of the PPB property. Therefore, IDL-PPBopt provides a promising scheme for the prediction and optimization of the PPB property and would be helpful for lead optimization of other pharmacokinetic properties.
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