An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules

Article Chemistry, Physical

Deep Neural Network Model to Predict the Electrostatic Parameters in the Polarizable Classical Drude Oscillator Force Field

Anmol Kumar et al.

Summary: The Drude polarizable force field captures electronic polarization effects and is useful for simulating biomolecules and other molecules. Deep neural network models can accurately predict molecular dipole moments and polarizabilities.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2022)

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Article Multidisciplinary Sciences

GEOM, energy-annotated molecular conformations for property prediction and molecular generation

Simon Axelrod et al.

Summary: Machine learning outperforms traditional approaches in molecular design. However, most ML models only predict molecular properties based on 2D chemical graphs or single 3D structures, neglecting the ensemble of 3D conformers accessible to a molecule. This article introduces a large-scale dataset, GEOM, which contains accurate conformers and experimental data annotations, aiming to facilitate the development of models predicting properties from conformer ensembles and generative models sampling 3D conformations.

SCIENTIFIC DATA (2022)

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Article Chemistry, Multidisciplinary

Automation of AMOEBA polarizable force field for small molecules: Poltype 2

Brandon Walker et al.

Summary: A next-generation protocol (Poltype 2) has been developed to automatically generate AMOEBA polarizable force field parameters for small molecules, with significant improvements in both features and computational efficiency. The protocol shows great potential for applications, particularly in predicting binding free energy in computational drug discovery.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2022)

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Review Chemistry, Multidisciplinary

Extendedtight-bindingquantum chemistry methods

Christoph Bannwarth et al.

Summary: This review discusses a family of atomistic, mostly quantum chemistry based semiempirical methods for describing large molecules in gas and condensed phase. These methods are derived from a density functional perturbation expansion and emphasize parameter availability for almost the entire periodic table, with improvements in atomic orbital basis set and interaction treatments. The methods are consistent with accurate gas phase theoretical reference data and have been extended for electronic spectra and response properties.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2021)

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Article Multidisciplinary Sciences

Quantum chemical benchmark databases of gold-standard dimer interaction energies

Alexander G. Donchev et al.

Summary: This paper introduces three benchmark collections of quantum mechanical data encompassing approximately 3,700 different types of noncovalent molecular interactions. These datasets can be useful for research in electronic structure theory and computational chemistry.

SCIENTIFIC DATA (2021)

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Article Chemistry, Medicinal

ParaMol: A Package for Automatic Parameterization of Molecular Mechanics Force Fields

Joao Morado et al.

Summary: The quality of force field parameterization plays a crucial role in determining the accuracy of observable properties computed from molecular mechanics simulations. ParaMol is a Python package focused on parameterizing bonded and nonbonded terms of druglike molecules by fitting to ab initio data. Through case studies, ParaMol demonstrates the ability to derive near-ideal parameters within the constraints of the functional form, while also discussing best practices and weighting methods for parameterization routes.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

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Article Chemistry, Physical

Optimal Solution to the Torsional Coefficient Fitting Problem in Force Field Parametrization

Adrian Kania et al.

Summary: Molecular modeling is a useful tool for studying biological systems at atomic scale, but optimizing force field parameters is crucial for accurately describing system behavior, such as phospholipid bilayer properties.

JOURNAL OF PHYSICAL CHEMISTRY A (2021)

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Article Chemistry, Physical

Cartesian message passing neural networks for directional properties: Fast and transferable atomic multipoles

Zachary L. Glick et al.

Summary: The study introduces a modified Cartesian MPNN framework for predicting atomic multipoles, demonstrating its accuracy and effectiveness on a large-scale dataset. The model accurately predicts atom-centered charges, dipoles, and quadrupoles, with minimal impact on multipole-multipole electrostatic energies, and is able to model the conformational dependencies of a molecule.

JOURNAL OF CHEMICAL PHYSICS (2021)

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Article Chemistry, Physical

OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space

Chao Lu et al.

Summary: The development and validation of the OPLS4 force field, which builds upon previous work with OPLS3e, improved model accuracy on challenging regimes of drug-like chemical space. A novel parametrization strategy for charged species was introduced, leading to improved accuracy on benchmarks assessing small-molecule solvation and protein-ligand binding.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

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Article Chemistry, Physical

A Scalable Graph Neural Network Method for Developing an Accurate Force Field of Large Flexible Organic Molecules

Xufei Wang et al.

Summary: A combination of physics-driven nonbonding potential and data-driven subgraph neural network bonding model has been successfully used for accurate and robust molecular mechanics simulations of organic polymers, offering a new path for developing next-generation organic force fields.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

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Article Chemistry, Physical

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Cheol Woo Park et al.

Summary: GNNFF framework accurately predicts atomic forces using machine learning, achieving high performance and computational speed across various material systems, and accurately predicting the forces of large MD systems after training on smaller systems. The Li diffusion coefficient obtained through MD simulation using this framework shows good accuracy compared to AIMD.

NPJ COMPUTATIONAL MATERIALS (2021)

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Article Biochemistry & Molecular Biology

PubChem in 2021: new data content and improved web interfaces

Sunghwan Kim et al.

Summary: PubChem, a popular chemical information resource, has made substantial improvements in the past two years by adding data from over 100 new sources, updating its homepage and record pages, introducing new services like the Periodic Table and Pathway pages, and creating a special data collection related to COVID-19 and SARS-CoV-2 in response to the pandemic.

NUCLEIC ACIDS RESEARCH (2021)

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PSI4 1.4: Open-source software for high-throughput quantum chemistry

Daniel G. A. Smith et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

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The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Justin S. Smith et al.

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TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

Xiang Gao et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

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AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials

Zachary L. Glick et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

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Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

Christian Devereux et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2020)

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GFN2-xTB-An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

Christoph Bannwarth et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

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A Robust and Cost-Efficient Scheme for Accurate Conformational Energies of Organic Molecules

Dmitry I. Sharapa et al.

CHEMPHYSCHEM (2019)

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Less is more: Sampling chemical space with active learning

Justin S. Smith et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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AMOEBA Polarizable Atomic Multipole Force Field for Nucleic Acids

Changsheng Zhang et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

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Review Biochemistry & Molecular Biology

Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding

Megan L. Peach et al.

JOURNAL OF MOLECULAR RECOGNITION (2017)

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Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals

Narbe Mardirossian et al.

MOLECULAR PHYSICS (2017)

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ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

J. S. Smith et al.

CHEMICAL SCIENCE (2017)

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Benchmark ab Initio Conformational Energies for the Proteinogenic Amino Acids through Explicitly Correlated Methods. Assessment of Density Functional Methods

Manoj K. Kesharwani et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)

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Transferability and additivity of dihedral parameters in polarizable and nonpolarizable empirical force fields

Marie Zgarbova et al.

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Automated Force Field Parameterization for Nonpolarizable and Polarizable Atomic Models Based on Ab Initio Target Data

Lei Huang et al.

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Polarizable Atomic Multipole-Based AMOEBA Force Field for Proteins

Yue Shi et al.

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Quantification of fuzzy chemical concepts: a computational perspective

Jerome F. Gonthier et al.

CHEMICAL SOCIETY REVIEWS (2012)

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Automation of AMOEBA polarizable force field parameterization for small molecules

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THEORETICAL CHEMISTRY ACCOUNTS (2012)

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Recent trends in conformational analysis

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2012)

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DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB)

Michael Gaus et al.

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Effect of the Damping Function in Dispersion Corrected Density Functional Theory

Stefan Grimme et al.

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CHARMM General Force Field: A Force Field for Drug-Like Molecules Compatible with the CHARMM All-Atom Additive Biological Force Fields

K. Vanommeslaeghe et al.

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Update of the CHARMM All-Atom Additive Force Field for Lipids: Validation on Six Lipid Types

Jeffery B. Klauda et al.

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Current Status of the AMOEBA Polarizable Force Field

Jay W. Ponder et al.

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Long-range corrected hybrid density functionals with damped atom-atom dispersion corrections

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The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals

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An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules

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