Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Mixed quantum–classical dynamics (MQCD) enables nonadiabatic simulations of molecular excited states by combining classical nuclear trajectories with quantum electronic-state population flow. The field is moving beyond benchmark small molecules toward nanoscopic systems, active environments, and long timescales—regimes where MQCD is stretched by three coupled demands: reliability, scalability, and flexibility in the electronic representation set by decoherence. Reliability means getting the controlling physics right: initial ensembles consistent with the experimental preparation, accurate potential energies and couplings driving the system's evolution, and correct nonadiabatic branching and recombination. Scalability demands reducing time-to-converged observables by curbing repeated high-level electronic-structure calls. Surrogate excited-state models (including machine-learning potentials) can replace much of the on-the-fly electronic structure, while active learning, multi-fidelity strategies, and uncertainty quantification guide where new reference data and additional sampling are actually needed. Flexibility of representation is essential in dense excited-state manifolds, where the decoherence-selected pointer basis may differ from the adiabatic energy basis; localized representations and open-quantum-system tools (Lindblad dynamics, stochastic unraveling) provide practical routes to encode that choice. Together, these directions define an agenda for MQCD that targets larger, longer, and more environmentally complex excited-state dynamics while retaining a trajectory-based workflow.

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Artificial intelligence (AI) is reshaping drug discovery by accelerating timelines and reducing costs, yet its impact remains constrained by a persistent gap between computational promise and translational delivery. This gap stems from upstream preclinical failures, including weak target validation, biologically irrelevant models, and insufficient accountability for overstated methodological claims that contribute to late-stage attrition. The Implementation, Methodology, Productivity, Assessment, Collaboration, Translation (IMPACT) framework addresses these root causes by establishing global standards that reinforce biological grounding, methodological credibility, and equitable collaboration. Implementation emphasizes Findable, Accessible, Interoperable, and Reusable (FAIR)-compliant datasets, standardized vocabularies, and clear gradients of AI involvement from assisted to fully AI-driven workflows. Methodology prioritizes reproducibility through model cards, containerized environments, and transparent reporting to support robust models. Productivity aligns AI efforts with urgent therapeutic priorities, including rare diseases, antimicrobial resistance, drug repurposing, and natural-product discovery. Assessment promotes rigorous benchmarking, blind validation, and uncertainty quantification, drawing on the long-established CASP model as a historical gold standard while critically examining emerging initiatives such as CACHE and Polaris Hub, which remain comparatively recent and evolving. Collaboration leverages federated learning, pre-competitive consortia, and interdisciplinary teams integrating AI specialists with domain experts. Translation ensures outputs are explainable, clinically relevant, ethically aligned, and regulatory-ready, consistent with emerging frameworks such as the FDA Draft Guidance on AI in Drug Development and the EU AI Act. By integrating technical standards with operational governance mechanisms, IMPACT provides a structured pathway toward transparent and translationally reliable AI-driven drug discovery.

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In this review we present eMap 2.0, a web-based application for predicting electron/hole transfer pathways in proteins and protein families based on their structures. The underlying model can be viewed as a coarse-grained version of the Pathways approach by Beratan and Onuchic [Beratan et al. J. Chem. Phys. 1987, 86, 4488]. Similar to the original framework, eMap employs graph-theory algorithms to search for the most efficient electron transfer pathways as shortest paths on a graph representation of the protein. In eMap, the nodes represent electron transfer active sites and only through-space tunneling is considered for each individual electron/hole hop. eMap 2.0 takes this model one step further by aiming at identifying shared electron transfer pathways in protein sets. From a graph theory standpoint, this is achieved using frequent subgraph mining (FSM) algorithms. Lastly, eMap 2.0 utilizes sequence and structural similarity measures to analyze and cluster the results. Here, we show how this robust method can be utilized to rapidly provide insights regarding conserved electron transfer pathways within protein families and to identify outliers, in which the conserved electron transfer pathway is blocked either by a mutation or conformational changes.

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The rapid evolution of machine learning (ML) has advanced materials discovery, providing tools to explore, predict, and design materials with tailored properties. Here we present an overview of emerging ML tools for data-driven materials innovation, including data curation, feature engineering, model development, interpretability, and inverse design. We highlight high-throughput material databases in providing large-scale, DFT-computed datasets, and discuss the importance of descriptor libraries that encode compositional and structural information into machine-readable inputs for model development. Advances in ML architectures, ranging from classical algorithms to graph neural networks, are discussed for their ability to capture complex structure–property relationships. Particular emphasis is given to inverse design frameworks using generative models and optimization strategies to enable property-targeted materials generation. We further explore interpretability and uncertainty quantification techniques that are important for bridging ML predictions with experimental validation. Automation platforms are described as tools for closed-loop, high-throughput discovery pipelines. We outline grand challenges, including data sparsity, model generalizability, and experimental integration. Finally, we summarize future directions that include foundation models pre-trained on broad, multimodal materials data; self-supervised learning strategies to reduce dependence on labeled datasets; ML workflows that embed thermodynamic and symmetry constraints to enhance interpretability; and fully autonomous laboratories that couple ML guidance with robotic synthesis and real-time feedback.

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The capability of anticipating and mitigating drug toxicity represents one of the most persistent challenges in drug development. Despite rigorous preclinical evaluation, nearly one third of drug candidates fail during the clinical phases due to safety issues, in particular hepatotoxicity and cardiotoxicity. Routine in vitro and in vivo toxicology tests, while essential to define safety margins, are frequently associated with high false-positive rates and poor translation to human outcomes. While investigative toxicology has improved mechanistic understanding of off-target interactions and toxicokinetics, the gap between preclinical findings and clinical safety still challenges efficient drug development. To overcome these drawbacks, the field is developing and applying more integrative strategies that combine computational modeling, machine learning, multi-omics technologies, and advanced in vitro systems. These approaches propose predictive pipelines likely able to identify chemical toxicology issues during early design stages and to characterize mechanisms of adverse events. In this review we provide a critical overview of emerging tools and strategies for drug toxicity prediction, evaluating their current impact, limitations, and translational potential to reduce safety-related attrition and support the development of safer therapeutics.

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S. Li, H. Du, H. Zhang, X. Zhang, Y. Ye, C. Shen, T. Hou, and P. Pan, “ Accelerating Covalent Drug Discovery: Recent Advances in Covalent Docking Tools,” Wiley Interdisciplinary Reviews. Computational Molecular Science 15, no. 6 (2025): e70062, https://doi.org/10.1002/wcms.70062.

In the "funding" section, the text "This work was supported by China Postdoctoral Science Foundation (Grant No. 2024M762886), National Natural Science Foundation of China (Grant Nos. 82204279, 82473843), Central Guidance for Local Science and Technology Development Funds Project (Grant No.2025ZY01022), and "Pioneer" and "Leading Goose" R&D Program of Zhejiang (Grant No. 2025C01117)." was incorrect.

This should have read as "This work was financially supported by "Pioneer" and "Leading Goose" R&D Program of Zhejiang (2025C01117), National Natural Science Foundation of China (82473843, 82204279), China Postdoctoral Science Foundation (2024M762886), and Central Guidance for Local Science and Technology Development Funds Project (2025ZY01022). We also thank the Information Technology Center and State Key Lab of CAD&CG, Zhejiang University for the support of computing resources."

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Molecular Dynamics (MD) has established itself as a pivotal computational tool across various scientific domains, including chemistry, biology, and materials science. Despite its widespread utility, MD faces inherent challenges, such as accuracy limitations, computational speed, and sampling efficiency. In recent years, machine learning, particularly deep learning, has seen significant advancements and is increasingly being integrated into MD processes. This review explores how deep learning can mitigate the issues associated with MD by addressing them from multiple angles. However, deep learning techniques introduce their own set of hurdles, including the need for extensive data, issues of interpretability, high computational costs, and concerns regarding transferability. Here, we discuss recent progress in the field of deep learning to overcome these obstacles. Ultimately, our goal is to demonstrate that, by leveraging the advancements made in both the MD and the machine learning community, deep learning has the potential to significantly enhance the capabilities of MD, paving the way to new scientific discovery.

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