Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments

Abstract

Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.

Introduction to the Special Issue: AI Meets Toxicology

“…The methods described in this special issue cover a wide range of AI methods ranging from expert systems, ,, over similarity measures including read-across methods, − to classical machine learning such as random forests (RF), support vector machines (SVM), and artificial neural networks (ANN) − ,, to deep learning (DL) methods ,,− , including equivariant neural networks, deep generative models, and even large language models . In addition to models relying purely on the chemical structure, there is a notable trend of bringing in additional modalities to improve or inform predictive models. , In the following, we provide an overview of the AI approaches used in the publications contained in the SI.…”

“…Most of the presented methods perform prediction tasks on molecules (for exceptions, see paragraphs below) and, thus, have to use representations of molecules. While for data handling, the simplified molecular-input line-entry system (SMILES) representation is often used, the predominant representations of molecules for modeling are still extended connectivity fingerprint (ECFP) or Morgan fingerprints. − ,− Several publications use graph neural networks that operate on the molecular graph. , Aside from the chemical structure, there is a growing tendency to incorporate biological characterizations and read-outs, for example, via cell morphology , or transcriptomics . The utilization of diverse representations, ranging from molecular structures to biological features, enhances the predictive models showcased in this section and could improve the comprehensive understanding of toxicological properties.…”

“…A number of approaches can be considered as expert systems, which are a class of traditional AI methods. As an example, Chakravarti developed hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments, demonstrating improved predictive performance compared to models based solely on either approach, thereby enhancing the specificity and coverage of structural alerts in in silico toxicology. Golden et al expanded a data set for skin sensitization and evaluated the performance of computational tools, which led to lower accuracies compared to previous estimates, with mispredictions and false positives highlighting the need for a more nuanced understanding of skin sensitization mechanisms beyond structural alerts and QSAR approaches.…”