Advancing Computational Toxicology by Interpretable Machine Learning

Abstract

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML-and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

“…However, reliance on large training datasets, lack of interpretability, and susceptibility to data biases remain as challenges. Ongoing research is focused on using explainable AI techniques to increase model interpretability (Jia et al 2023 ). Overall, as chemically diverse and multi-modal toxicological datasets grow, AI and DL show immense potential to transform predictive toxicology.…”

ToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science

“…While AI promises to be transformative for toxicology, there are certain challenges that need to be proactively addressed to responsibly translate AI's potential into impactful applications. (Jia et al, 2023). While AI models like deep neural networks achieve high predictive performance, their inner workings are often opaque, making them hard to interpret.…”

Comparative Study of Machine Learning-Based QSAR Modeling of Anti-inflammatory Compounds from Durian Extraction

“…Several QSAR models have been proposed to investigate the antioxidant activity of flavonoids. − Although linear models have offered useful insights, they face difficulties in explaining the complex relationship between structural factors and antioxidant activity. , In this regard, machine learning algorithms, such as random forests (RFs), support vector regression (SVR), and artificial neural networks (ANNs), will be introduced to offer enhanced predictive capabilities; additionally, the QSAR with machine learning models can help over new information from the existing data. − For instance, in a study conducted by Li et al, they attained an impressive coefficient of determination ( R 2 ) of 0.807 by employing four significant descriptors and the multiple linear regression (MLR) method to forecast the antioxidant activity of polysaccharides. Furthermore, a comparative analysis of results from the multilayer perceptron artificial neural network (MLP-ANN) model revealed even more precise predictions, showcasing an exceptional R 2 value of 0.944 …”