Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

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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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.

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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.

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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.

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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.

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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.

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