Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

We present a tutorial review of the theoretical background and a step-by-step computational procedure for determining kinetic isotope effects (KIEs) of chemical reactions in aqueous solution. The method combines path integral and free energy perturbation (PI-FEP) simulations to directly yield the ratio of the partition functions between different isotopic reactions. This review is the result of collaborative work in a Computational Chemistry course at the University of Minnesota, where two intramolecular proton-transfer reactions were given as classroom exercises. Through this study, we wish to accomplish three main goals: (i) determination of nuclear quantum effects and quantum-mechanical potentials of mean force (QM-PMF), (ii) computation of primary KIE using PI-FEP simulations, and (iii) an understanding of solvent effects on proton-transfer reactions in water. Analyses of computational results provide insights into substituent effects on chemical reactivity, solvent effects on reaction rate, nuclear quantum effects on free energy barrier, and KIEs on transition state. The theory and computational procedure for determining KIE can be directly used to study chemical reactions in solutions and enzymatic processes with two publicly available software packages (CHARMM and QBICS).

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Z. Guo, Z. Huang, Q. Chen, et al. “ByteQC: GPU-Accelerated Quantum Chemistry Package for Large-Scale Systems,” Wiley Interdisciplinary Reviews: Computational Molecular Science 15 (2025): e70034, https://doi.org/10.1002/wcms.70034

In the originally published article, the affiliations of the sixth and ninth authors were incorrectly listed, and one of the affiliations of the ninth author was missing from the affiliation list.

The correct affiliations of the authors are as follows:

Hung Q. Pham³

³ByteDance Seed, San Jose, California, USA

Ji Chen^4,5,6

⁴School of Physics, Peking University, Beijing, China

⁵Interdisciplinary Institute of Light-Element Quantum Materials and Research Center for Light-Element Advanced Materials, Peking University, Beijing, China

⁶Frontiers Science Center for Nano-Optoelectronics, Peking University, Beijing, China

We apologize for this error.

The cover image is based on the article Computation of Protein Thermostability and Epistasis by Gonzalo Jimenez-Oses et al., https://doi.org/10.1002/wcms.70045.

Explainable artificial intelligence (XAI) is increasingly essential in drug discovery, where interpretability and trust must accompany predictive accuracy. As deep learning models, particularly, deep neural networks (DNNs) and graph neural networks (GNNs), enhance molecular property prediction, de novo design, and toxicity estimation, transparent, mechanistically meaningful insights become critical. This article classifies major XAI strategies in computational molecular science, including gradient-based attribution, perturbation analysis, surrogate modeling, counterfactual reasoning, and self-explaining architectures. Molecular representations, such as fingerprints, SMILES, molecular graphs, and latent embeddings, are evaluated for their impact on explanation fidelity. An evaluation framework is outlined using metrics like fidelity, stability, completeness, sparsity, and usability, with emphasis on integration into drug discovery workflows. The discussion also highlights emerging directions, including neuro-symbolic systems and physics-informed networks that embed mechanistic constraints into statistical models. By aligning algorithmic transparency with pharmacological reasoning, XAI not only demystifies black-box models but also supports scientific insight, regulatory compliance, and ethical AI deployment in pharmaceutical research.

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By bridging molecular-level insights with macroscopic performance metrics, computational strategies are poised to transform how we design next-generation 3D-printable materials with enhanced precision, functionality, and sustainability. We present a critical overview examining the role of computational methods in advancing the design and application of 3D-printable polymers. We cover key considerations—including solvation behavior, viscosity, gel point, mechanical properties, and polymer structure—as well as the design of new polymer functionalities. We highlight how a spectrum of physics-based methods, ranging from quantum chemical to coarse-grained simulations, can be leveraged to interrogate relevant polymer properties at multiple scales. In particular, we illustrate the growing impact of machine learning in accelerating polymer discovery and optimization. Such methods, whether applied independently or integrated into multi-scale modeling frameworks, offer powerful tools for pre-screening and selecting optimal formulations tailored to diverse 3D printing technologies and applications. Although challenges remain to integrate different approaches into workable prediction pipelines, the rate of advance and improvements in methods, data interoperability, and data quality, offer great promise of a ‘concept to print’ pipeline in the future.

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Heterogeneous catalysis has a wide range of applications in chemical manufacturing and sustainable technologies. It uses solid catalysis to enable efficient chemical transformations. Traditional research on active sites and reaction mechanisms relies heavily on experiments and computational methods, such as density functional theory calculations. However, the volume of scientific literature and data is growing fast. This rapid growth has made it increasingly difficult to capture, process, and act on emerging insights systematically. Recently, large language models (LLMs) have emerged as powerful tools to support various stages in catalysis research. Their ability to understand and generate natural language helps them extract useful information from vast amounts of text, assist in catalyst design, aid in planning experiments, and clarify complex descriptors. In this advanced review, we first analyze recent progress in applying LLMs to heterogeneous catalysis, focusing on four key areas: literature mining and knowledge extraction, catalyst design and screening, experiment automation and workflow optimization, and the interpretation of high-dimensional descriptors. We then highlight the challenges in this field despite these advances, most notably the need for domain-specific fine-tuning and the improvement of molecular representation. We conclude by discussing future opportunities for integrating LLMs with complementary machine learning approaches and expert-in-the-loop systems, toward accelerating the rational discovery of next-generation catalysts.

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The large and steadily increasing volume of scientific publications presents a challenge in accessing and utilizing data due to their unstructured nature. Toxicology, in particular, depends on structured data from diverse study types for study evaluation, weight-of-evidence chemical assessments, and validation of new approach methodologies (NAMs). Manual data extraction is time and labor-intensive. This work presents an automated data extraction workflow using large language models (LLMs) within the KNIME platform. The workflow integrates document parsing tools with LLMs to extract variables from scientific publications and general PDF files. Two execution modes are available: text mode and image mode. Text mode applies tools for extracting text and tables, while image mode uses multimodal LLMs to process non-linear layouts and graphical content. The workflow achieves 81.14% accuracy in text mode for scientific publications and up to 98.54% in image mode for general PDF files. The KNIME platform ensures accessibility through a user-friendly interface, allowing non-experts to use advanced data extraction methods. This automated approach facilitates toxicological research by improving the retrieval of structured data. By democratizing access to LLM-powered workflows, this approach paves the way for significant advancements in knowledge synthesis to support biomedical research.

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