On the use of chemical bonding descriptors in machine learning

2025/11/25 by Michele Tomasini, Lucia Caporaso, Martí Gimferrer +1 · 1 citation
Materials Science · Computer Science · #Machine Learning in Materials Science #Computational Drug Discovery Methods #Advanced Graph Neural Networks

paper · doi:10.1016/j.ccr.2025.217383

Abstract

This review explores recent advances in machine learning in chemistry, emphasizing mechanistic understanding, performance optimization, and emerging design strategies. Key developments include novel synthesis routes, computational screening, hybrid experimental–theoretical approaches, and in-situ characterization. The review highlights how these innovations improve efficiency, selectivity, and scalability while uncovering fundamental structure-activity relationships. Special attention is given to integrating predictive modeling and high-throughput experimentation, which accelerates discovery cycles and enables rational design. Comparative discussions of different methodologies reveal synergies between traditional approaches and data-driven tools. Despite remarkable progress, translating laboratory results into practical applications remains a central challenge. The review concludes by outlining open questions, methodological gaps, and future research directions aimed at developing robust, cost-effective, and environmentally sustainable solutions.

Citations

• Charge, bond order and valence in the AB initio SCF theory
• Quantum-chemical insights from deep tensor neural networks
• Challenges in olefin metathesis: past, present and future
• Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• A simple measure of electron localization in atomic and molecular systems
• The Activated Complex in Chemical Reactions

Cited by

• Quaternary ammonium salt ionic liquids as catalysts in the cycloaddition reaction of epoxides with CO2 in the presence of hydrogen bond donors

Related

• Integration of Machine Learning in the Characterization of Hydrogen Bonding Analysis: A Comparative Study
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• Molecular Machine Learning in Chemical Process Design
• Advances of Machine Learning in Molecular Modeling and Simulation
• From 2015 to 2023: How Machine Learning Aids Natural Product Analysis