Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Drug design involves the process of identifying and designing novel molecules that have desirable properties and bind well to a given target receptor. Typically, such molecules are identified by screening large chemical libraries for desirable physicochemical properties and binding strength with the target protein. This traditional approach, however, has severe limitations as exhaustively screening every molecule in known chemical libraries is computationally infeasible. Furthermore, currently available molecular libraries are only a minuscule part of the entire set of possible drug-like molecular structures (drug-like chemical space). In this review, we discuss how the former limitation is addressed by modeling virtual screening as a search space problem and how these endeavors utilize machine learning to reduce the number of required computational experiments to identify top candidates. We follow that up by discussing generative methods that attempt to approximate the entire drug-like chemical space providing us a path to explore beyond the known drug-like chemical space. We place special emphasis on generative models that learn the marginal distributions conditioned on specific properties or receptor structures for efficient sampling of molecules. Through this review, we aim to highlight modern machine learning based methods that try to efficiently enhance our sampling capability beyond conventional screening methods which, in turn, would benefit drug design significantly. Therefore, we also encourage further methods of development that work on such important aspects of drug design.

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The scalable preparation of high-quality and low-cost two-dimensional (2D) materials is critical to achieving their potential applications in various fields. Chemical vapor deposition (CVD) method is considered the most promising method for producing ultrathin 2D materials and has continued to develop in recent years. First-principles calculations have provided important theoretical guidance for the CVD synthesis of 2D materials, and have played an increasingly important role in the field of material synthesis in recent years. In this review, we present recent advances in the growth mechanism of 2D materials, focusing on the theoretical research progress of four typical 2D materials: graphene, hexagonal boron nitride (hBN), transition metal dichalcogenide (TMDC), and phosphorene. Several aspects of the growth process are discussed in detail, including the decomposition of precursors, nucleation, growth kinetics, domain shape, and epitaxial and alignment of 2D crystals. Based on the understanding of these atomic-scale growth processes, strategies toward the wafer-scale growth of continuous and homogeneous 2D thin films are proposed and confirmed by experiments. In the final section, we summarize future challenges and opportunities in the computational studies of the growth mechanism of 2D materials.

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The cover image is based on the Advanced Review Machine intelligence for chemical reaction space by Philippe Schwaller et al., https://doi.org/10.1002/wcms.1604.

While in principle exact, Kohn–Sham density functional theory—the workhorse of computational chemistry—must rely on approximations for the exchange–correlation functional. Despite staggering successes, present-day approximations still struggle when the effects of electron–electron correlation play a prominent role. The limit in which the electronic Coulomb repulsion completely dominates the exchange–correlation functional offers a well-defined mathematical framework that provides insight for new approximations able to deal with strong correlation. In particular, the mathematical structure of this limit, which is now well-established thanks to its reformulation as an optimal transport problem, points to the use of very different ingredients (or features) with respect to the traditional ones used in present approximations. We focus on strategies to use these new ingredients to build approximations for computational chemistry and highlight future promising directions.

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The cover image is based on the Advanced Review The hierarchy of Davydov's Ansätze and its applications by Yang Zhao et al., https://doi.org/10.1002/wcms.1589.

The cover image is based on the Advanced Review Along the allostery stream: Recent advances in computational methods for allosteric drug discovery by Duan Ni et al., https://doi.org/10.1002/wcms.1585.

Every day, density-functional theory (DFT) is routinely applied to computational modeling of molecules and materials with the expectation of high accuracy. However, in certain situations, popular density-functional approximations (DFAs) have the potential to give substantial quantitative, and even qualitative, errors. The most common class of error is delocalization error, which is an overarching term that also encompasses the one-electron self-interaction error. In our opinion, its resolution remains the greatest outstanding challenge in DFT development. In this paper, we review the history of delocalization error and provide several complimentary conceptual pictures for its interpretation, along with illustrative examples of its various manifestations. Approaches to reduce delocalization error are discussed, as is its interplay with other shortcomings of popular DFAs, including treatment of non-bonded repulsion and neglect of London dispersion.

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The Martini model, a coarse-grained force field for molecular dynamics simulations, has been around for nearly two decades. Originally developed for lipid-based systems by the groups of Marrink and Tieleman, the Martini model has over the years been extended as a community effort to the current level of a general-purpose force field. Apart from the obvious benefit of a reduction in computational cost, the popularity of the model is largely due to the systematic yet intuitive building-block approach that underlies the model, as well as the open nature of the development and its continuous validation. The easy implementation in the widely used Gromacs software suite has also been instrumental. Since its conception in 2002, the Martini model underwent a gradual refinement of the bead interactions and a widening scope of applications. In this review, we look back at this development, culminating with the release of the Martini 3 version in 2021. The power of the model is illustrated with key examples of recent important findings in biological and material sciences enabled with Martini, as well as examples from areas where coarse-grained resolution is essential, namely high-throughput applications, systems with large complexity, and simulations approaching the scale of whole cells.

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In 2020, a “hybrid” expert-AI computer program called Chematica (a.k.a. Synthia) was shown to autonomously plan multistep syntheses of complex natural products, which remain outside the reach of purely data-driven AI programs. The ability to plan at this level of chemical sophistication has been attributed mainly to the superior quality of Chematica's reactions rules. However, rules alone are not sufficient for advanced synthetic planning which also requires appropriately crafted algorithms with which to intelligently navigate the enormous networks of synthetic possibilities, score the synthetic positions encountered, and rank the pathways identified. Chematica's algorithms are distinct from prêt-à-porter algorithmic solutions and are product of multiple rounds of improvements, against target structures of increasing complexity. Since descriptions of these improvements have been scattered among several of our prior publications, the aim of the current Review is to narrate the development process in a more comprehensive manner.

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Adenylyl cyclases (ACs) play a key role in many signaling cascades. ACs catalyze the production of cyclic AMP from ATP and this function is stimulated or inhibited by the binding of their cognate stimulatory or inhibitory Gα subunits, respectively. Here we used simulation tools to uncover the molecular and subcellular mechanisms of AC function, with a focus on the AC5 isoform, extensively studied experimentally. First, quantum mechanical/molecular mechanical free energy simulations were used to investigate the enzymatic reaction and its changes upon point mutations. Next, molecular dynamics simulations were employed to assess the catalytic state in the presence or absence of Gα subunits. This led to the identification of an inactive state of the enzyme that is present whenever an inhibitory Gα is associated, independent of the presence of a stimulatory Gα. In addition, the use of coevolution-guided multiscale simulations revealed that the binding of Gα subunits reshapes the free-energy landscape of the AC5 enzyme by following the classical population-shift paradigm. Finally, Brownian dynamics simulations provided forward rate constants for the binding of Gα subunits to AC5, consistent with the ability of the protein to perform coincidence detection effectively. Our calculations also pointed to strong similarities between AC5 and other AC isoforms, including AC1 and AC6. Findings from the molecular simulations were used along with experimental data as constraints for systems biology modeling of a specific AC5-triggered neuronal cascade to investigate how the dynamics of downstream signaling depend on initial receptor activation.

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