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1. Learning molecular potentials with neural networks

   https://doi.org/10.1002/wcms.1564  

   Gokcan, Hatice ; Isayev, Olexandr ( July 2021 , WIREs Computational Molecular Science)

   The potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry. However, the proper choice of the computational approach based on computational cost and reliability of calculated energies is a dilemma, especially for large molecules. This dilemma is proved to be even more problematic for studies that require hundreds and thousands of calculations, such as drug discovery. On the other hand, driven by their pattern recognition capabilities, neural networks started to gain popularity in the computational chemistry community. During the last decade, many neural network potentials have been developed to predict a variety of chemical information of different systems. Neural network potentials are proved to predict chemical properties with accuracy comparable to quantum mechanical approaches but with the cost approaching molecular mechanics calculations. As a result, the development of more reliable, transferable, and extensible neural network potentials became an attractive field of study for researchers. In this review, we outlined an overview of the status of current neural network potentials and strategies to improve their accuracy. We provide recent examples of studies that prove the applicability of these potentials. We also discuss the capabilities and shortcomings of the current models and the challenges and future aspects of their development and applications. It is expected that this review would provide guidance for the development of neural network potentials and the exploitation of their applicability.

   This article is categorized under:

   Data Science > Artificial Intelligence/Machine Learning

   Molecular and Statistical Mechanics > Molecular Interactions

   Software > Molecular Modeling

    

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2. Brownian dynamics simulations of biomolecular diffusional association processes

   https://doi.org/10.1002/wcms.1649  

   Muñiz‐Chicharro, Abraham ; Votapka, Lane W. ; Amaro, Rommie E. ; Wade, Rebecca C. ( December 2022 , WIREs Computational Molecular Science)

   Brownian dynamics (BD) is a computational method to simulate molecular diffusion processes. Although the BD method has been developed over several decades and is well established, new methodological developments are improving its accuracy, widening its scope, and increasing its application. In biological applications, BD is used to investigate the diffusive behavior of molecules subject to forces due to intermolecular interactions or interactions with material surfaces. BD can be used to compute rate constants for diffusional association, generate structures of encounter complexes for molecular binding partners, and examine the transport properties of geometrically complex molecules. Often, a series of simulations is performed, for example, for different protein mutants or environmental conditions, so that the effects of the changes on diffusional properties can be estimated. While biomolecules are commonly described at atomic resolution and internal molecular motions are typically neglected, coarse‐graining and the treatment of conformational flexibility are increasingly employed. Software packages for BD simulations of biomolecules are growing in capabilities, with several new packages providing novel features that expand the range of questions that can be addressed. These advances, when used in concert with experiment or other simulation methods, such as molecular dynamics, open new opportunities for application to biochemical and biological systems. Here, we review some of the latest developments in the theory, methods, software, and applications of BD simulations to study biomolecular diffusional association processes and provide a perspective on their future use and application to outstanding challenges in biology, bioengineering, and biomedicine.

   This article is categorized under:

   Structure and Mechanism > Computational Biochemistry and Biophysics

   Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

   Software > Simulation Methods

    

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3. Data-driven many-body potentials from density functional theory for aqueous phase chemistry

   https://doi.org/10.1063/5.0129613  

   Palos, Etienne ; Dasgupta, Saswata ; Lambros, Eleftherios ; Paesani, Francesco ( March 2023 , Chemical Physics Reviews)

   Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body (MB) interactions is in order, are often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underlying DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/MB molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and QM/MB-MM simulations in condensed phases. 

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4. AQME: Automated quantum mechanical environments for researchers and educators

   https://doi.org/10.1002/wcms.1663  

   Alegre‐Requena, Juan V. ; Sowndarya S. V., Shree ; Pérez‐Soto, Raúl ; Alturaifi, Turki M. ; Paton, Robert S. ( February 2023 , WIREs Computational Molecular Science)

   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:

   Data Science > Chemoinformatics

   Data Science > Computer Algorithms and Programming

   Software > Quantum Chemistry

    

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5. PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics

   https://doi.org/10.1063/5.0219851  

   Pederson, John P ; McDaniel, Jesse G ( July 2024 , The Journal of Chemical Physics)

   PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100 ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule’s electron density.

    

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