Authors: Yi-Zhou Sun | Mougui Tang | Shuoqing Zhang | Hong Xin
Source: Chinese Science Bulletin (Chinese Version) (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

数据驱动的有机分子理化性质预测 <sec><p indent="0mm">The physicochemical properties of molecules, such as frontier orbital energy levels, bond energies, and spectroscopic characteristics, form the basis for understanding and predicting molecular chemical behavior. For instance, based on the energy levels and arrangement of frontier orbitals, chemists can effectively determine electron donors and acceptors in chemical reactions, and rationally assess reaction activity and selectivity, widely applied in a series of organic molecular transformations including pericyclic reactions. Chemical bond energies provide core thermodynamic parameters for the process of bond breaking and formation, which are crucial for precisely understanding reaction thermodynamics and kinetics. UV, IR, and NMR spectroscopic features offer effective means to describe the geometric and electronic structures of molecules. Therefore, an accurate understanding of molecular physicochemical properties is one of the important strategies for humans to explore the molecular world, which not only helps in deeply understanding the microscopic mechanisms of chemical reactions but also effectively guides the design and development of new reactions, making it a long-term focus of chemical scientific research. </sec><sec> Traditionally, molecular properties were determined through experimental measurements and theoretical calculations. While experiments provide a reliable foundation, they are often constrained by cost, efficiency, and the need for specialized equipment. Computational chemistry, bolstered by advancements in computer hardware, has emerged as a complementary approach, extending the reach of molecular property research through simulations of electronic structure and reaction dynamics. However, computational methods also face challenges in achieving precision and efficiency in complex molecular systems. </sec><sec> With the continuous accumulation of chemical data and significant advancements in artificial intelligence (AI) technology, machine learning methods have made remarkable progress in chemistry, showing important potential in the prediction of molecular physicochemical properties. Based on large-scale molecular property data and innovative molecular modeling architectures, AI models can successfully capture the high-dimensional relationship between molecular structure and physicochemical properties, and accurately predict molecular properties accordingly. On representative molecular property datasets like QM9, cutting-edge machine learning models have achieved prediction accuracy comparable to traditional quantum calculations, while also achieving geometric progression in computational efficiency. Intelligent prediction of molecular properties provides a new strategy for chemists to understand and explore the chemical world, which will not only accelerate the pace of chemical research but also offer new assistance to related disciplines such as pharmaceuticals and materials. </sec><sec> This paper mainly introduces the recent progress in AI prediction of the physicochemical properties of organic molecules. The first part outlines the progress of related databases and datasets, including NIST, PubChemQC, SDBS, iBonD, FreeSolv, QM9, PubChemQC PM6, and Frag20, detailing their content and scope. The second part describes the basic workflow of machine learning modeling for molecular properties, outlining the specific steps of dataset establishment, molecular encoding, model training, application, and analysis, along with recent progress in machine learning methods. The third part discusses the relevant progress in machine learning prediction for representative scenarios such as spectroscopic properties, orbital energies, acid dissociation constant (p<italic>K</italic><sub>a</sub>) values, bond dissociation energies (BDEs), and nucleophilicity parameters. In these key aspects of molecular properties, machine learning technology has demonstrated exceptional ability in terms of precision and efficiency. Finally, the paper summarizes the current state of the field, discusses some of the key challenges and limitations faced by the field, and looks forward to the prospects for field development from three directions: constructing more comprehensive and standardized molecular property databases, promoting the interaction between prediction models and experimental chemists, and enhancing the interpretability and generalizability of AI models. </sec> Computer science Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Advanced Physical and Chemical Molecular Interactions