Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

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.

This article is categorized under:

The dramatic progress of experimental attosecond science has called for the development of new theoretical and computational tools capable of accurately model the correlated electron dynamics triggered by attosecond molecular photoionization. We describe the nature and the main outcome of this development, with particular focus on the B-spline ADC and RCS-ADC ab initio methods.

This article is categorized under:

Aerosol particles are important for our global climate, but the mechanisms and especially the relative importance of various vapors for new particles formation (NPF) remain uncertain. Quantum chemical (QC) studies on organic enhanced nucleation has for the past couple of decades attracted immense attention, but very little remains known about the exact organic compounds that potentially are important for NPF. Here we comprehensively review the QC literature on atmospheric cluster formation involving organic compounds. We outline the potential cluster systems that should be further investigated. Cluster formation involving complex multi-functional organic accretion products warrant further investigations, but such systems are out of reach with currently applied methodologies. We suggest a “cluster of functional groups” approach to address this issue, which will allow for the identification of the potential structure of organic compounds that are involved in atmospheric NPF.

This article is categorized under:

Recent years have witnessed an increasing interest in time-dependent coupled-cluster (TDCC) theory for simulating laser-driven electronic dynamics in atoms and molecules, and for simulating molecular vibrational dynamics. Starting from the time-dependent bivariational principle, we review different flavors of single-reference TDCC theory with either orthonormal static, orthonormal time-dependent, or biorthonormal time-dependent spin orbitals. The time-dependent extension of equation-of-motion coupled-cluster theory is also discussed, along with the applications of TDCC methods to the calculation of linear absorption spectra, linear and low-order nonlinear response functions, highly nonlinear high harmonic generation spectra and ionization dynamics. In addition, the role of TDCC theory in finite-temperature many-body quantum mechanics is briefly described along with a few other application areas.

This article is categorized under:

Quantum chemistry (QC) is one of the most promising applications of quantum computing. However, present quantum processing units (QPUs) are still subject to large errors. Therefore, noisy intermediate-scale quantum (NISQ) hardware is limited in terms of qubit counts/circuit depths. Variational quantum eigensolver (VQE) algorithms can potentially overcome such issues. Here, we introduce the OpenVQE open-source QC package. It provides tools for using and developing chemically-inspired adaptive methods derived from unitary coupled cluster (UCC). It facilitates the development and testing of VQE algorithms and is able to use the Atos Quantum Learning Machine (QLM), a general quantum programming framework enabling to write/optimize/simulate quantum computing programs. We present a specific, freely available QLM open-source module, myQLM-fermion. We review its key tools for facilitating QC computations (fermionic second quantization, fermion-spin transforms, etc.). OpenVQE largely extends the QLM's QC capabilities by providing: (i) the functions to generate the different types of excitations beyond the commonly used UCCSD ansatz; (ii) a new Python implementation of the “adaptive derivative assembled pseudo-Trotter method” (ADAPT-VQE). Interoperability with other major quantum programming frameworks is ensured thanks to the myQLM-interop package, which allows users to build their own code and easily execute it on existing QPUs. The combined OpenVQE/myQLM-fermion libraries facilitate the implementation, testing and development of variational quantum algorithms, while offering access to large molecules as the noiseless Schrödinger-style dense simulator can reach up to 41 qubits for any circuit. Extensive benchmarks are provided for molecules associated to qubit counts ranging from 4 to 24. We focus on reaching chemical accuracy, reducing the number of circuit gates and optimizing parameters and operators between “fixed-length” UCC and ADAPT-VQE ansätze.

This article is categorized under:

AQME, automated quantum mechanical environments, is a free and open-source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi-empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble-averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre-configured workflows are available in various formats, and hands-on video tutorials illustrate their use.

This article is categorized under:

Nanofluidics research has achieved a significant growth over the past few years. New phenomena of nanoscaled fluid flows are being reported continuously, such as altered liquid properties, fast flows, and ion rectification, which attract tremendous research interests in many fields, such as membrane science, biological nanochips, and energy conventions. Multiscale simulations, covering quantum mechanics, molecular mechanics, coarse-grained particle dynamics (mesoscale), and continuum mechanics, have shown their great advantages in studying the new frontier of nanofluidics in academia and industry, which is in range of 1–1000 nm scale. These simulations provide the opportunity to visualize the nanofluidics applications existed in the minds of scientists and then guide experimental design to realize the potential of nanofluidics applications in industrial. In this article, we attempt to give a comprehensive review of nanofluidics from the aspect of multiscale simulations. The methodology and role of various simulation methods used in the investigation of nanofluidics are presented. The properties and characteristics of nanofluidics are summarized. The applications of nanofluidics in recent years are emphasized. And then the development of simulation methods and the application of nanofluidics are also prospected.

This article is categorized under:

Metal ion batteries (MIBs), represented by lithium ion batteries are important energy storage devices for storing renewable energy. Advanced development of MIBs depends on the exploration of efficient and sustainable electrode materials. Organic electrode materials (OEMs) with redox-active moieties are low-cost and eco-friendly alternatives to conventional inorganic electrode materials for MIBs. Computational simulation plays an important role in understanding the energy storage mechanism of different active functional groups and boosting the discovery of new OEMs for high-efficient MIBs. Here, we will review recent progress of OEMs and comprehensively survey factors that determine their electrochemical properties. Dependable computational methods to guide the design of OEMs are comprehensively discussed and machine learning is highlighted as an emerging method to reveal the underlying structure–performance relationship and facilitate screening of OEMs with high-efficiency. Finally, we summarize the available molecular design strategies to effectively improve the redox activity and stability of OEMs, and discuss challenges and opportunities of theoretical calculations of OEMs for MIBs.

This article is categorized under:

We present a discussion of recent progress in excited-state-specific quantum chemistry and quantum Monte Carlo alongside a demonstration of how a combination of methods from these two fields can offer reliably accurate excited state predictions across singly excited, doubly excited, and charge transfer states. Both of these fields have seen important advances supporting excited state simulation in recent years, including the introduction of more effective excited-state-specific optimization methods, improved handling of complicated wave function forms, and ways of explicitly balancing the quality of wave functions for ground and excited states. To emphasize the promise that exists at this intersection, we provide demonstrations using a combination of excited-state-specific complete active space self-consistent field theory, selected configuration interaction, and state-specific variance minimization. These demonstrations show that combining excited-state-specific quantum chemistry and variational Monte Carlo can be more reliably accurate than either equation of motion coupled cluster theory or multi-reference perturbation theory, and that it can offer new clarity in cases where existing high-level methods do not agree.

This article is categorized under: