Advancing Predictive Risk Assessment of Chemicals via Integrating Machine Learning, Computational Modeling, and Chemical/Nano‐Quantitative Structure‐Activity Relationship Approaches

Abstract

The escalating use of novel chemicals and nanomaterials (NMs) across diverse sectors underscores the need for advanced risk assessment methods to safeguard human health and the environment. Traditional labor‐intensive approaches have given way to computational methods. This review integrates recent developments in chemical and nano‐quantitative structure‐activity relationship (QSAR) with machine learning and computational modeling, offering a comprehensive predictive assessment of NMs and chemicals. It explores nanodescriptors, their role in predicting toxicity, and the amalgamation of machine learning algorithms with chemical and nano‐QSAR for improved risk assessment accuracy. The article also investigates computational modeling techniques like molecular dynamics simulations, molecular docking, and molecular mechanics/quantum mechanics for predicting physical and chemical properties. By consolidating these approaches, the review advocates for a more accurate and efficient means of assessing risks associated with NMs/chemicals, promoting their safe utilization and minimizing adverse effects on human health and the environment. A valuable resource for researchers and practitioners, informed decision‐making, advancing our understanding of potential risks, is facilitated. Beyond studying systems at various scales, computational modeling integrates data from diverse sources, enhancing risk assessment accuracy and fostering the safe use of NMs/chemicals while minimizing their impact on human health and the environment.

Keywords

• chemical QSAR
• computational modeling
• machine learning
• nanodescriptors and cellular toxicity
• nano‐QSAR