Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Most spin density functionals are collinear, assuming the spin magnetization has only one nonzero component. However, a fully defined functional should be noncollinear, treating all three components of the spin magnetization vector as variables. The multicollinear approach is introduced to bridge this gap by generalizing an arbitrary collinear functional to a noncollinear one. In contrast to the traditional scheme, which adopts the local projection of the spin magnetization vector field, the multicollinear method employs a global projection scheme. It offers several key advantages, including recovering the collinear limit, ensuring global spin rotational invariance, maintaining numerical stability, and providing nonzero local torque. Its broad applicability spans relativistic and nonrelativistic cases, molecular and periodic systems, ground and excited states, as well as static and dynamic simulations. Furthermore, for collinear systems, it provides capabilities that go beyond standard collinear functionals by establishing a rigorous framework for spin-flip TDDFT. This makes it a powerful tool for treating challenging problems such as double excitations, conical intersections, bond dissociation, and diradicals. Overall, the multicollinear approach provides a unified and versatile framework for quantum chemistry.

This article is categorized under:

• Electronic Structure Theory > Density Functional Theory

Covalent inhibitors have garnered renewed attention in recent years, with their rational design becoming increasingly critical in drug discovery. Among the technologies facilitating the discovery of covalent inhibitors, covalent docking has emerged as a pivotal tool in various stages of drug development including virtual screening, lead optimization, and mechanistic studies. Since its inception as an extension of conventional docking methods in the early 2000s, covalent docking tools have undergone substantial advancements. This review provides a comprehensive overview of covalent docking algorithms, systematically categorizing their approaches according to covalent bond formation, which primarily include tethered docking, biased docking, and dynamic covalent docking approaches. A comparative analysis of current covalent docking tools is provided, alongside a critical discussion of remaining challenges. Special emphasis is placed on the growing impact of artificial intelligence (AI) in shaping novel methodologies and expanding the capabilities of covalent docking. Finally, we discuss prospects for advancing covalent docking methodologies and their applications in drug discovery.

This article is categorized under:

• Software > Molecular Modeling

For more than two decades, weighted ensemble (WE) path sampling strategies have enabled the simulation of pathways for rare events—or barrier-crossing processes—with significantly less computing cost than conventional simulations, all while preserving rigorous kinetics. This review highlights recent advances in WE methods and software, including tools for mechanistic analysis of path ensembles and efficient estimation of rates. We showcase successful WE applications across a wide range of condensed-phase processes, such as hybrid quantum mechanics/molecular mechanics (QM/MM) simulations of microsecond-timescale chemical reactions, and atomistic simulations of slower processes on the millisecond to seconds timescale. These applications span drug membrane permeation, ligand unbinding, and the large-scale opening of the SARS-CoV-2 spike protein. We also discuss the current limitations and key challenges facing WE strategies, which have yet to reach their full potential.

This article is categorized under:

• Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods
• Software > Simulation Methods
• Structure and Mechanism > Computational Biochemistry and Biophysics

This review provides a comprehensive overview of the paradigm shift for computer-aided molecular design and property predictions from similarity-based modeling, including quantitative structure–activity/property relationship (QSAR/QSPR), read-across, read-across structure–activity relationship (RASAR), and pharmacophore mapping to sequence-based chemical language models (CLMs) using deep learning techniques. Starting with multiple methods of chemical structure and latent chemical space representations and touching the molecular descriptor- and fingerprint-based classical type modeling, this review introduces string-based deep learning models involving techniques like recurrent neural networks (RNNs) with long short-term memory (LSTM) and other architectures such as variational autoencoder (VAE), attention models, and generative adversarial networks (GANs). The basics of more efficient transformer models are also discussed. The problem-solving of training with scarce data using transfer learning, data augmentation, and natural-product-inspired training is analyzed. The applications of CLMs in the de novo design of small molecules of medicinal interest, enzymes, peptides, and multitask agents, the predictions of properties of drug candidates, and activity cliffs are presented. The applications of CLMs in materials science and predictive toxicology are also mentioned. We discuss the limitations of feature-based modeling approaches confined to a restricted feature space. In contrast, CLMs lack specific insights into aspects like SARs, bioisosteric replacements, synthesizability, and so forth, which collectively hinder their regulatory acceptance and acceptance by synthetic chemists. This review concludes that cheminformaticians need to utilize two complementary approaches, where factors like simplicity, reproducibility, and regulatory acceptability may prompt the use of feature-based approaches while aiming for higher accuracy and generating novel molecules may drive toward adopting CLMs.

This article is categorized under:

• Data Science > Chemoinformatics
• Structure and Mechanism > Computational Biochemistry and Biophysics
• Software > Molecular Modeling

Polypharmacy has become a routine practice in modern medicine, yet the risks of drug–drug interactions (DDIs) remain a critical challenge for patient safety. Given the vast number of possible drug combinations and the impracticality of exhaustive clinical testing, computational approaches have become indispensable for DDI prediction. Over the past 15 years, the field has shifted from handcrafted, similarity-based models to deep learning and graph neural networks (GNNs). Prediction tasks have also expanded from binary classification to multi-class, multi-label, cold-start, and higher-order settings. These reflect an emerging paradigm in both methodology and scope. Yet critical bottlenecks remain. Data sparsity, unreliable negatives, class imbalance, and source heterogeneity undermine robustness; models still struggle with generalization to unseen drugs, with mechanistic interpretability, and with capturing asymmetric or higher-order interactions. These limitations continue to impede translation into clinical and regulatory practice. In this Advanced Review, we critically assess methodological evolution, benchmark datasets, and emerging paradigms, including GNNs, large language models (including multimodal extensions), and generative AI, and examine their promises and limitations. We argue that next-generation progress hinges on unified multimodal and mechanism-aware frameworks, strategies for robust learning under cold-start and long-tail scenarios, and the integration of causal inference with generative approaches to enhance interpretability. By synthesizing past advances with forward-looking perspectives, this review outlines strategic pathways for accelerating the transition of DDI prediction toward intelligent, interpretable, and clinically actionable solutions.

This article is categorized under:

• Data Science > Artificial Intelligence/Machine Learning
• Data Science > Chemoinformatics
• Molecular and Statistical Mechanics > Molecular Interactions