Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

Reinforcement learning (RL) is one important branch of artificial intelligence (AI), which intuitively imitates the learning style of human beings. It is commonly derived from solving game playing problems and is extensively used for decision-making, control and optimization problems. It has been extensively applied for solving complicated problems with the property of Markov decision-making processes. With data accumulation and comprehensive analysis, researchers are not only satisfied with predicting the results for experimental systems but also hope to design or control them for the sake of obtaining the desired properties or functions. RL is potentially facilitated to solve a large number of complicated biological and chemical problems, because they could be decomposed into multi-step decision-making process. In practice, substantial progress has been made in the application of RL to the field of biomedicine. In this paper, we will first give a brief description about RL, including its definition, basic theory and different type of methods. Then we will review some detailed applications in various domains, for example, molecular design, reaction planning, molecular simulation and etc. In the end, we will summarize the essentialities of RL approaches to solve more diverse problems compared with other machine learning methods and also outlook the possible trends to overcome their limitations in the future.

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Orbital-free density functional theory (OFDFT) stands out as a many-body electronic structure approach with a low computational cost that scales linearly with system size, making it well suitable for large-scale simulations. The past decades have witnessed impressive progress in OFDFT, which opens a new avenue to capture the complexity of realistic systems (e.g., solids, liquids, and warm dense matters) and provide a complete description of some complicated physical phenomena under realistic conditions (e.g., dislocation mobility, ductile processes, and vacancy diffusion). In this review, we first present a concise summary of the major methodological advances in OFDFT, placing particular emphasis on kinetic energy density functional and the schemes to evaluate the electron–ion interaction energy. We then give a brief overview of the current status of OFDFT developments in finite-temperature and time-dependent regimes, as well as our developed OFDFT-based software package, named by ATLAS. Finally, we highlight perspectives for further development in this fascinating field, including the major outstanding issues to be solved and forthcoming opportunities to explore large-scale materials.

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Structure-based drug design is a widely applied approach in the discovery of new lead compounds for known therapeutic targets. In most structure-based drug design applications, the docking procedure is considered the crucial step. Here, a potential ligand is fitted into the binding site, and a scoring function assesses its binding capability. With the rise of modern machine-learning in drug discovery, novel scoring functions using machine-learning techniques achieved significant performance gains in virtual screening and ligand optimization tasks on retrospective data. However, real-world applications of these methods are still limited. Missing success stories in prospective applications are one reason for this. Additionally, the fast-evolving nature of the field makes it challenging to assess the advantages of each individual method. This review will highlight recent strides toward improved real world applicability of machine-learning based scoring, enabling a better understanding of the potential benefits and pitfalls of these functions on a project. Furthermore, a systematic way of classifying machine-learning based scoring that facilitates comparisons will be presented.

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For dealing with complex reaction (CR) systems that show typical chemical phenomena in molecular aggregation states, the Red Moon (RM) approach is introduced based on a new efficient and systematic RM methodology. First, the theoretical background with my motivation to develop the RM approach is presented from the recent necessity to perform ‘atomistic’ molecular simulation of large-scale and long-term phenomena of (i) complex chemical reactions, (ii) stereospecificity, and (iii) aggregation structures. The RM methodology uses both the molecular dynamics (MD) method for molecular motions (translation, rotation, and vibration of molecules) that frequently occur on a short-time scale and the Monte Carlo (MC) method for rare events such as chemical reactions that hardly do on that time scale. Then, under the transition rate using both the potential energy difference before and after a rare event trial and its chemical kinetic probability, it is tested and judged by the MC method whether the trial is possible (Metropolis method). Next, typical applications of the RM approach are reviewed in two main research fields, (i) polymerization and (ii) storage battery (rechargeable battery or secondary cell), with various examples of our successful studies. Finally, we conclude that the RM approach using the RM methodology should become an efficient new-generation approach as one promising computational molecular strategy (CMT). We believe it will play an essential role in surveying, at the multilevel resolution, various specificities of CR systems in molecular aggregation states.

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Time-resolved photoelectron spectroscopy is a powerful pump-probe technique which can probe nonadiabatic dynamics in molecules. Interpretation of the experimental signals however requires input from theoretical simulations. Advances in electronic structure theory, nonadiabatic dynamics, and theory to calculate the ionization yields, have enabled accurate simulation of time-resolved photoelectron spectra leading to successful applications of the technique. We review the basic theory and steps involved in calculating time-resolved photoelectron spectra, and highlight successful applications.

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This paper focuses on the computationally assisted design of molecularly imprinted polymers (MIP), emphasizing the selected strategies and chosen methods of approach. In summary, this paper provides an overview of the MIP fabrication procedure, focusing on key factors and challenges, where the fabrication of MIP includes a step-by-step process with extensive experimental procedures. This brings challenges in optimizing experimental conditions, such as the selection of monomer, cross-linker, and their relevant molar ratios to the template and solvent. Next, the principles of computational methods are elucidated to explore their potential applicability in solving the challenges. The computational approach can tackle the problems and optimize the MIP's design. Finally, the atomistic, quantum mechanical (QM), and combined methods in the recent research studies are overviewed with stress on strategies, analyses, and results. It is demonstrated that optimization of pre-polymerization mixture by employing simulations significantly reduces the trial-and-error experiments. Besides, higher selectivity and sensitivity of MIP are observed. The polymerization and resulting binding sites by computational methods are considered. Several models of binding sites are formed and analyzed to assess the affinities representing the sensitivity and selectivity of modeled cavities. Combined QM/atomistic methods showed more flexibility and versatility for realistic modeling with higher accuracy. This methodological advancement aligns with the principles of green chemistry, offering cost-effective and time-efficient solutions in MIP design.

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Molecular dynamics (MD) simulations can provide detailed insights into complex molecular systems, such as DNA, at high resolution in space and time. Using current computer architectures, time scales of tens of microseconds are feasible with contemporary all-atom force fields. However, these timescales are insufficient to accurately characterize large conformational transitions in DNA and compare calculations to experimental data. This review discusses the advantages and drawbacks of two simulation approaches to overcome the timescale challenge. The first approach is based on adding biasing potentials to the system to drive transitions. Umbrella sampling, steered MD, and metadynamics are examples of these methods. A key challenge of such methods is the necessity of selecting one or a few efficient coordinates, commonly referred to as collective variables (CVs), along which to apply the biasing potential. The path-metadynamics methodology addresses this issue by finding the optimal route(s) between states in a multi-dimensional CV space. The second strategy is path sampling, which focuses MD simulations on the transitions. The assumption is that even though transitions between states are rare, they are generally fast. Stopping the simulations as soon as they reach a stable state can significantly increase simulation efficiency. We introduce these methods on the two-dimensional Müller–Brown potential. DNA applications are featured for two different processes: the Watson–Crick–Franklin to Hoogsteen transition in adenine–thymine base pairs and the binding of a DNA-binding protein domain to DNA.

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Generative AI is rapidly transforming the frontier of research in computational structural biology. Indeed, recent successes have substantially advanced protein design and drug discovery. One of the key methodologies underlying these advances is diffusion models (DM). Diffusion models originated in computer vision, rapidly taking over image generation and offering superior quality and performance. These models were subsequently extended and modified for uses in other areas including computational structural biology. DMs are well equipped to model high dimensional, geometric data while exploiting key strengths of deep learning. In structural biology, for example, they have achieved state-of-the-art results on protein 3D structure generation and small molecule docking. This review covers the basics of diffusion models, associated modeling choices regarding molecular representations, generation capabilities, prevailing heuristics, as well as key limitations and forthcoming refinements. We also provide best practices around evaluation procedures to help establish rigorous benchmarking and evaluation. The review is intended to provide a fresh view into the state-of-the-art as well as highlight its potentials and current challenges of recent generative techniques in computational structural biology.

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Stereochemical effects significantly influence chemical processes, yet it is not well understood if they are a leading source of uncertainty in combustion modeling. Stereochemistry influences a combustion model (i) at the earliest stage of its construction when mapping the reaction network, (ii) in the computation of individual thermochemical and rate parameters, and (iii) in the prediction of combustion observables. The present work reviews the importance of enumerating stereochemical species and reactions at each of these steps. Further, it analyzes the separate influence of several types of stereochemistry, including geometric, optical, and fleeting transition state diastereomers. Three reaction networks serve to examine which stages of low-temperature oxidation are most affected by stereochemistry, including the first and second oxidation of n-butane, the third oxidation of n-pentane, and the early stages of pyrolysis of 1- and 2-pentene. The 149 reactions in the n-butane mechanism are expanded to 183 reactions when accounting for diastereomerism. Each of these 183 reactions is parameterized with ab initio kinetics computations to determine that, for the n-butane mechanism, the median factor of diastereomeric deviation is 3.5 at 360 K for rate constants and as high as 1.6 for mechanism reactivity, in terms of ignition delay times, as opposed to a mechanism without stereochemical expansion.

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Ab-initio quantum chemistry simulations are essential for understanding electronic structure of molecules and materials in almost all areas of chemistry. A broad variety of electronic structure theories and implementations has been developed in the past decades to hopefully solve the many-body Schrödinger equation in an approximate manner on modern computers. In this review, we present recent progress in advancing low-rank electronic structure methodologies that rely on the wavefunction sparsity and compressibility to select the important subset of electronic configurations for both weakly and strongly correlated molecules. Representative chemistry applications that require the many-body treatment beyond traditional density functional approximations are discussed. The low-rank electronic structure theories have further prompted us to highlight compressive and expressive principles that are useful to catalyze idea of quantum learning models. The intersection of the low-rank correlated feature design and the modern deep neural network learning provides new feasibilities to predict chemically accurate correlation energies of unknown molecules that are not represented in the training dataset. The results by others and us are discussed to reveal that the electronic feature sets from an extremely low-rank correlation representation, which is very poor for explicit energy computation, are however sufficiently expressive for capturing and transferring electron correlation patterns across distinct molecular compositions, bond types and geometries.

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