Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

For the simulations of polymeric systems, coarse-grained (CG) molecular dynamics simulations are computationally demanding not only because of their high computational efficiency, but also these CG models can provide sufficient structural and dynamical properties at both micro- and meso-scopic levels. During the past decades, developments of these CG models are roughly in two directions, that is, generic and chemically system-specific models. The developme of the formmer focuses on the capability of the model to capature the general properties of the system, for instance, scaling relations between both structural and dynamic properties with respect to chain length. On the other hand, to bridging the gap between physics and chemistry, chemically-specifi models are also widely developed which are able to retain the inherent chemical–physical properties for a given polymer system. However, due to the reduction of atomistic degree of freedom a faithful reproduction of structure and especialy dynamics properties of the system is the maijor challenge. In this review, after a brief introduction of some widely used generic models, we present an overview of both recent achievements and remainning challendges in the development of chemically-specific CG approaches, for the simulations of polymer systems.

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Designing functional molecules is the prerogative of experts who have advanced knowledge and experience in their fields. To democratize automatic molecular design for both experts and nonexperts, we introduce a generic open-sourced framework, ChemTSv2, to design molecules based on a de novo molecule generator equipped with an easy-to-use interface. Besides, ChemTSv2 can easily be integrated with various simulation packages, such as Gaussian 16 package, and supports a massively parallel exploration that accelerates molecular designs. We exhibit the potential of molecular design with ChemTSv2, including previous work, such as chromophores, fluorophores, drugs, and so forth. ChemTSv2 contributes to democratizing inverse molecule design in various disciplines relevant to chemistry.

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Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery. However, it is still critical for their adoption by the medicinal chemistry community to achieve models that, in addition to achieving high performance in their predictions, can be trusty explained to the end users in terms of their knowledge and background. Therefore, the investigation and development of explainable artificial intelligence (XAI) methods have become a key topic to address this challenge. For this reason, a comprehensive literature review about explanation methodologies for AI based models, focused in the field of drug discovery, is provided. In particular, an intuitive overview about each family of XAI approaches, such as those based on feature attribution, graph topologies, or counterfactual reasoning, oriented to a wide audience without a strong background in the AI discipline is introduced. As the main contribution, we propose a new taxonomy of the current XAI methods, which take into account specific issues related with the typical representations and computational problems study in the design of molecules. Additionally, we also present the main visualization strategies designed for supporting XAI approaches in the chemical domain. We conclude with key ideas about each method category, thoroughly providing insightful analysis about the guidelines and potential benefits of their adoption in medical chemistry.

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Ferroelectrics with electrically switchable spontaneous polarizations can be used for information storage, where a low switching barrier is favorable to reduce the energy cost and enhance the speed for data writing. Meanwhile their robustness at working temperature should be ensured, which is a challenge for the designs of low-barrier ferroelectrics. Here we review several types of ferroelectric mechanisms that may render both low switching barriers and room-temperature robustness, which have been theoretically proposed in previous studies. (1) The prediction of sliding ferroelectricity with ultralow switching barriers has been experimentally confirmed in a series of van der Waals layers, which may enable convenient electrical control of various physical properties in 2D materials, like magnetic, photovoltaic, valleytronic and topological properties. (2) Hydrogen-bonded ferroelectricity spontaneously formed by head-to-tail chains can be switched by proton-transfer crossing a low barrier, and a mechanism of ultra-high piezoelectricity utilizing the specific features of hydrogen bonding has been proposed. (3) High-ionicity ferroelectricity induced by covalent-like ionic bondings may entail high polarizations and low barriers during switching, which is attributed to the features of long-range Coulomb interaction, and the long ion-displacements crossing unitcell may give rise to unconventional ferroelectricity with quantized polarizations even in crystals of non-ferroelectric point groups. Those low-barrier ferroelectric mechanisms may bring in both new physics and technological advances, which are to be further explored.

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Biological membranes (biomembranes) are one of the most complicated structures that allow life to exist. Investigating their structure, dynamics, and function is crucial for advancing our knowledge of cellular mechanisms and developing novel therapeutic strategies. However, experimental investigation of many biomembrane phenomena is challenging due to their compositional and structural complexity, as well as the inherently multi-scalar features. Computational approaches, particularly molecular dynamics (MD) simulations, have emerged as powerful tools for addressing the atomic details of biomembrane systems, driving breakthroughs in our understanding of biomembranes and their roles in cellular function. This review presents an overview of the latest advancements in related computational approaches, from force fields and model construction to MD simulations and trajectory analysis. We also discussed current hot research topics and challenges. Finally, we outline future directions, emphasizing the integration of force field development, enhanced sampling techniques, and data-driven approaches to accelerate the growth of this field in the years to come. We aim to equip readers with an understanding of the promise and limitations of emerging computational technologies in biomembrane systems and offer valuable recommendations for future research endeavors.

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Hybrid halide perovskite solar cells have been recognized as one of the most promising future photovoltaic technologies due to their demonstrated high-power conversion efficiency, versatile stoichiometry and low cost. However, degradation caused by environmental exposure and structural instability due to ionic defect migration hinders the commercialization of this technology. While the experimental studies try to understand the phenomenology of the degradation mechanisms and devise practical measures to improve the stability of these materials, theoretical studies have attempted to bridge the gaps in our understanding of the fundamental degradation mechanisms at different time and length scales. A deeper understanding of the physical and chemical phenomena at an atomic level through multiscale materials modeling is going to be crucial for the knowledge-based prognosis and design of future halide perovskites. There have been increased efforts in this direction in the last few years. However, the instability fundamentals explored through atomistic modeling and simulation methods have not been reviewed comprehensively in the literature yet. Therefore, this paper is an attempt to present a critical review, while identifying the existing gaps and opportunities in the investigation of the degradation and instability issues of the halide perovskites using computational methods. The review will primarily focus on the instability caused due to the intrinsic ionic defect migration and degradation due to thermal, moisture and light exposure. The findings from the simulation studies conducted primarily using density functional theory, ab initio molecular dynamics, classical molecular dynamics and machine learning methods will be presented.

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Recent efforts to synthetically expand drug-like chemical libraries have led to the emergence of unprecedently large virtual databases. This surge of make-on-demand molecular datasets has been received enthusiastically across the drug discovery community as a new paradigm. In several recent studies, virtual screening (VS) of larger make-on-demand collections resulted in the identification of novel molecules with higher potency and specificity compared to more conventional VS campaigns relying on smaller in-stock libraries. These results inspired ultra-large VS against various clinically relevant targets, including key proteins of the SARS-CoV-2 virus. As library sizes rapidly surpassed the billion compounds mark, new computational screening strategies emerged, shifting from conventional docking to fragment-based and machine learning-accelerated methods. These approaches significantly reduce computational demands of ultra-large screenings by lowering the number of molecules explicitly docked onto a target. Such strategies already demonstrated promise in evaluating libraries of tens of billions of molecules at relatively low computational cost. Herein, we review recent advancements in structure-based methods for ultra-large virtual screening that drug discovery practitioners have adopted to explore the ever-expanding chemical universe.

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We describe the development, implementation, and application of a polarizable QM/MM strategy, based on the AMOEBA polarizable force field, for calculating molecular properties and performing dynamics of molecular systems embedded in complex matrices. We show that polarizable QM/MM is a well-understood, mature technology that can be deployed using a state-of-the-art implementation that combines efficient numerical methods and linear scaling techniques. Thanks to these numerical advances and to the availability of parameters for a wide manifold of systems in the AMOEBA force field, polarizable QM/AMOEBA can be used for advanced production applications, that range from the prediction of spectroscopies to ground- and excited-state multiscale ab initio molecular dynamics simulations.

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We present MultiPsi, an open-source MCSCF program for the calculation of ground and excited states properties of strongly correlated systems. The program currently implements a general MCSCF code with excited states available using either state-averaging or linear response. It is written in a highly modular fashion using Python/C++ which makes it well suited as a development platform, enabling easy prototyping of novel methods, and as a teaching tool using interactive notebooks. The code is also very efficient and designed for modern high-performance computing environments using hybrid OpenMP/MPI parallelization. This efficiency is demonstrated with the calculation of the CASSCF energy and linear response of a molecule with more than 700 atoms as well as a fully optimized conventional CI calculation on more than 400 billion determinants.

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Drug discovery is a daunting and failure-prone task. A critical process in this research field is represented by the biological target and pocket identification steps as they heavily determine the subsequent efforts in selecting a putative ligand, most often a small molecule. Finding “ligandable” pockets, namely protein cavities that may accept a drug-like binder is instrumental to the more general and drug discovery oriented “druggability” estimation process. While high-throughput experimental techniques exist to identify putative binding sites other than the orthosteric one, these techniques are relatively expensive and not so commonly available in labs. In this regard, computational means of detecting ligandable pockets are advisable for their inexpensiveness and speed. These methods can become, in principle, particularly predictive when supported by machine learning methodologies that provide the modeling framework. As with any data-driven effort, the outcome critically depends on the input data, its featurization process and possible associated biases. Also, the machine learning task, (supervised/unsupervised) the learning method, and the possible usage of molecular dynamics data considerably shape the inherent assumptions of the modeling step. Defining a proper quantitative thermodynamic and/or kinetic score (or label) is key to the modeling process; here we revise literature and propose residence time as a novel ideal indicator of ligandability. Interestingly the vast majority of the methods does not keep into consideration kinetics nor thermodynamics when devising predictors.

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