Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

The catalytic CO₂ hydrogenation to produce valuable fuels and chemicals holds immense importance in addressing energy scarcity and environmental degradation. Given that the real catalytic reaction system is complex and dynamic, the structure of catalysts might experience dynamic evolution under real reaction conditions. It implies that the real active sites might only generated during the reaction process. The induction factor of dynamic evolution of active sites could be reactants, intermediates, products, and other local chemical environments. Utilizing in-situ/operando characterization techniques allows for the real-time observation of the dynamic evolution process, further combining multiscale theoretical simulations can effectively reveal the refined structure of real active sites and catalytic mechanisms. Herein, we summarized the latest advancements in understanding the dynamic active sites and catalytic mechanisms during the real reaction process for the CO₂ hydrogenation to C₁ products (CH₃OH, CO, and CH₄). The dynamic evolutions of the catalyst in morphology, size, valence state, and interface between active component and support were discussed, respectively. Future research could benefit from more in-situ characterization and theoretical simulation to explore the microstructure and reaction mechanism, aiming to produce high conversion and selectivity catalysts for CO₂ hydrogenation reactions.

Molecular quantum chemistry has seen enormous progress in the last few decades thanks to more advanced and sophisticated numerical techniques and computing power. Following the recent interest in extending these capabilities to condensed-phase problems, we summarize basic knowledge of condensed-phase quantum chemistry for readers with experience in molecular quantum chemistry. We highlight recent efforts in this direction, including solving the electron repulsion integrals bottleneck, implementing hybrid density functional theory and wavefunction methods, and simulating lattice dynamics for periodic systems within atom-centered basis sets. Many computational techniques presented here are inspired by the extensive method developments rooted in quantum chemistry. In this Focus Article, we selectively focus on the computational techniques rooted in molecular quantum chemistry, emphasize some challenges, and point out open questions. We hope our perspectives will encourage researchers to pursue this exciting and promising research avenue.

In recent decades, coupled cluster theory has proven valuable in accurately describing correlation in many-body systems, particularly in time-independent computations of molecular electronic structure and vibrations. This review describes recent advancements in using coupled cluster parameterizations for time-dependent wave functions for the efficient computation of the quantum dynamics associated with the motion of nuclei. It covers time-dependent vibrational coupled cluster (TDVCC) and time-dependent modal vibrational coupled cluster (TDMVCC), which employ static and adaptive basis sets, respectively. We discuss the theoretical foundation, including many-mode second quantization, bivariational principles, and various parameterizations of time-dependent bases. Additionally, we highlight key features that make TDMVCC promising for future quantum dynamical simulations. These features include fast configuration-space convergence, the use of a compact adaptive basis set, and the possibility of efficient implementations with a computational cost that scales only polynomially with system size.

Drug toxicity and market withdrawals are two issues that often obstruct the lengthy and intricate drug discovery process. In order to enhance drug effectiveness and safety, this review examines withdrawn drugs and presents a novel paradigm for their redesign. In addition to addressing methodological issues with toxicity datasets, this study highlights important shortcomings in in silico drug toxicity prediction models and suggests solutions. High-throughput screening (HTS) has greatly progressed with the advent of 3D organoid and organ-on-chip (OoC) technologies, which provide physiologically appropriate systems that replicate the structure and function of human tissue. These systems provide accurate, human-relevant data for drug development, toxicity evaluation, and disease modeling, overcoming the limitations of traditional 2D cell cultures and animal models. Their integration into HTS pipelines has shown to have a major influence, promoting drug redesign efforts and enabling improved accuracy in preclinical research. The potential of fragment-based drug discovery to enhance pharmacokinetics (PK) and pharmacodynamics (PD) when combined with conventional techniques is highlighted in this study. The limits of animal models are discussed, with a focus on the need of bioengineered humanized systems such OoC technologies and 3D organoids. To improve drug candidate screening and simulate real illnesses, advanced models are crucial. This leads to improved target affinity and fewer adverse effects.

Singlet fission (SF) is a down-conversion photophysical process involving transforming a high-energy singlet state into two lower-energy triplet excitons. It has attracted extensive attention over the past two decades because of its potential to break the power conversion limit in photovoltaic devices. However, this material's complex, strongly correlated electronic properties and its various packing structures pose challenges to understanding its intrinsic mechanisms and limiting theory-guided molecular design. In this review, we summarize our theoretical work by studying the electronic structure, exciton-phonon structure and low-excited state dynamics of several typical materials, clearly elucidating the microscopic mechanism of the SF process. Subsequently, based on an in-depth understanding of the mechanism, we use the novel macrocyclic framework to design intramolecular SF candidates and hope to improve the energy conversion efficiency of SF-based photovoltaic devices.

The latest developments of a general exploration/exploitation strategy for the computational study of molecular bricks of life in the gas-phase are presented and illustrated by means of prototypical semi-rigid and flexible systems. In the first step, generalized natural internal coordinates are employed to obtain a clear-cut separation between different degrees of freedom, and machine-learning algorithms based on chemical descriptors (synthons) drive fast quantum chemical methods in the exploration of rugged potential energy surfaces ruled by soft degrees of freedom. Then, different quantum chemical models are carefully selected for exploiting energies, geometries, and vibrational frequencies with the aim of maximizing the accuracy of the overall description while retaining a reasonable cost for all the steps. In particular, a composite wave-function method is used for energies, whereas a double-hybrid functional is employed for geometries and harmonic frequencies and a cheaper global hybrid functional for anharmonic contributions. A panel of molecular bricks of life containing up to 50 atoms is employed to show that the proposed strategy draws closer to the accuracy of state-of-the-art composite wave-function methods for small semi-rigid molecules, but is applicable to much larger systems. The implementation of the whole computational workflow in terms of preprocessing and postprocessing of data provided by standard electronic structure codes paves the way toward the accurate yet not prohibitively expensive study of medium- to large-sized molecules by a user-friendly black-box tool exploitable also by experiment-oriented researchers.

The hundreds of density functional theory (DFT) methods developed over the past three decades are often referred to as the “zoo” of DFT approximations. In line with this terminology, the numerous DFT benchmark studies might be considered the “safari” of DFT evaluation efforts, reflecting their abundance, diversity, and wide range of application and methodological aspects. These benchmarks have played a critical role in establishing DFT as the dominant approach in quantum chemical applications and remain essential for selecting an appropriate DFT method for specific chemical properties (e.g., reaction energy, barrier height, or noncovalent interaction energy) and systems (e.g., organic, inorganic, or organometallic). DFT benchmark studies are a vital tool for both DFT users in method selection and DFT developers in method design and parameterization. This review provides best-practice guidance on key methodological aspects of DFT benchmarking, such as the quality of benchmark reference values, dataset size, reference geometries, basis sets, statistical analysis, and electronic availability of the benchmark data. Additionally, we present a flowchart to assist users in systematically choosing these methodological aspects, thereby enhancing the reliability and reproducibility of DFT benchmarking studies.

Active matters, which consume energy to exert mechanical forces, include molecular motors, synthetic nanomachines, actively propelled bacteria, and viruses. A series of unique phenomena emerge when active matters interact with cellular interfaces. Activity changes the mechanism of nanoparticle intracellular delivery, while active mechanical processes generated in the cytoskeleton play a major role in membrane protein distribution and transport. This review provides a comprehensive overview of the theoretical and simulation models used to study these nonequilibrium phenomena, offering insights into how activity enhances cellular uptake, influences membrane deformation, and governs surface transport dynamics. Furthermore, we explore the impact of membrane properties, such as fluidity and viscosity, on transport efficiency and discuss the slippage dynamics and active rotation behaviors on the membrane surface. The interplay of active particles and membranes highlights the essential role of nonequilibrium dynamics in cellular transport processes, with potential applications in drug delivery and nanotechnology. Finally, we provide an outlook highlighting the significance of deeper theoretical and simulation-based investigations to optimize active particles and understand their behavior in complex biological environments.

Molecular electrostatic potential (MESP) analysis has emerged as a pivotal tool in drug discovery, providing insights into molecular reactivity and noncovalent interactions essential for drug function. While widely used MESP-on-isodensity surface analysis offers interpretations of electron-rich or deficient regions of a drug molecule, the MESP topology parameters such as spatial minimum (V_min) and MESP at nuclei (V_n) provide a quantitative understanding. The investigation into the correlation between MESP parameters and various molecular properties such as lipophilicity, pK_a (acidity/basicity), conformations, and tautomeric forms is crucial for understanding the impact on biological activity of drugs and facilitating drug design. Moreover, MESP topology analysis serves as a fundamental tool in elucidating the pharmacological behavior of compounds and optimizing their therapeutic efficacy. A quantitative study utilizing V_n parameters to assess the hydrogen bond propensity of a drug presents a novel strategy for investigating drug-receptor interactions with increased precision. The qualitative and quantitative analysis of the MESP features of various drugs, including their applications in cancer, tuberculosis, tumors, inflammation, and infectious diseases such as malaria, bacterial infections, fungal infections, and viral infections, is conducted in this review.