Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations.

ABSTRACT: The use of machine learning techniques in computational chemistry has gained significant momentum since large molecular databases are now readily available. Predictions of molecular properties using machine learning have advantages over the traditional quantum mechanics calculations because they can be cheaper computationally without losing the accuracy. We present a new extrapolatable and explainable molecular representation based on bonds, angles and dihedrals that can be used to train machine learning models. The trained models can accurately predict the electronic energy and the free energy of small organic molecules with atom types C, H N and O, with a mean absolute error of 1.2 kcal mol^-1. The models can be extrapolated to larger organic molecules with an average error of less than 3.7 kcal mol^-1 for 10 or fewer heavy atoms, which represent a chemical space two orders of magnitude larger. The rapid energy predictions of multiple molecules, up to 7 times faster than previous ML models of similar accuracy, has been achieved by sampling geometries around the potential energy surface minima. Therefore, the input geometries do not have to be located precisely on the minima and we show that accurate density functional theory energy predictions can be made from force-field optimised geometries with a mean absolute error 2.5 kcal mol^-1.

SUBMITTER: Lee S

PROVIDER: S-EPMC9214916 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields.

Project description:The widely used CHARMM additive all-atom force field includes parameters for proteins, nucleic acids, lipids, and carbohydrates. In the present article, an extension of the CHARMM force field to drug-like molecules is presented. The resulting CHARMM General Force Field (CGenFF) covers a wide range of chemical groups present in biomolecules and drug-like molecules, including a large number of heterocyclic scaffolds. The parametrization philosophy behind the force field focuses on quality at the expense of transferability, with the implementation concentrating on an extensible force field. Statistics related to the quality of the parametrization with a focus on experimental validation are presented. Additionally, the parametrization procedure, described fully in the present article in the context of the model systems, pyrrolidine, and 3-phenoxymethylpyrrolidine will allow users to readily extend the force field to chemical groups that are not explicitly covered in the force field as well as add functional groups to and link together molecules already available in the force field. CGenFF thus makes it possible to perform "all-CHARMM" simulations on drug-target interactions thereby extending the utility of CHARMM force fields to medicinally relevant systems.

| S-EPMC2888302 | biostudies-literature

New Force-Field for Organosilicon Molecules in the Liquid Phase.

Project description:In this paper, we present a new molecular model that can accurately predict thermodynamic liquid state and phase-change properties for organosilicon molecules including several functional groups (alkylsilane, alkoxysilane, siloxane, and silanol). These molecules are of great importance in geological processes, biological systems, and material science, yet no force field currently exists that is widely applicable to organosilicates. The model is parametrized according to the recent Polarization-Consistent Approach (PolCA), which allows for polarization effects to be incorporated into a nonpolarizable model through post facto correction terms and is therefore consistent with previous parametrizations of the PolCA force field. Alkyl groups are described by the United-Atom approach, bond and angle parameters were taken from previous literature studies, dihedral parameters were fitted to new quantum chemical energy profiles, point charges were calculated from quantum chemical optimizations in a continuum solvent, and Lennard-Jones dispersion/repulsion parameters were fitted to match the density and enthalpy of vaporization of a small number of selected compounds. Extensive validation efforts were carried out, after careful collection and curation of experimental data for organosilicates. Overall, the model performed quite well for the density, enthalpy of vaporization, dielectric constant, and self-diffusion coefficient, but it slightly overestimated the magnitude of self-solvation free energies. The modular and transferable nature of the PolCA force field allows for further extensions to other types of silicon-containing compounds.

| S-EPMC8679648 | biostudies-literature

Automation of AMOEBA polarizable force field parameterization for small molecules.

Project description:A protocol to generate parameters for the AMOEBA polarizable force field for small organic molecules has been established, and polarizable atomic typing utility, Poltype, which fully automates this process, has been implemented. For validation, we have compared with quantum mechanical calculations of molecular dipole moments, optimized geometry, electrostatic potential, and conformational energy for a variety of neutral and charged organic molecules, as well as dimer interaction energies of a set of amino acid side chain model compounds. Furthermore, parameters obtained in gas phase are substantiated in liquid-phase simulations. The hydration free energy (HFE) of neutral and charged molecules have been calculated and compared with experimental values. The RMS error for the HFE of neutral molecules is less than 1 kcal/mol. Meanwhile, the relative error in the predicted HFE of salts (cations and anions) is less than 3% with a correlation coefficient of 0.95. Overall, the performance of Poltype is satisfactory and provides a convenient utility for applications such as drug discovery. Further improvement can be achieved by the systematic study of various organic compounds, particularly ionic molecules, and refinement and expansion of the parameter database.

| S-EPMC3322661 | biostudies-literature

Polarizable Multipole-Based Force Field for Aromatic Molecules and Nucleobases.

Project description:Aromatic molecules with π electrons are commonly involved in chemical and biological recognitions. For example, nucleobases play central roles in DNA/RNA structure and their interactions with proteins. The delocalization of the π electrons is responsible for the high polarizability of aromatic molecules. In this work, the AMOEBA force field has been developed and applied to 5 regular nucleobases and 12 aromatic molecules. The permanent electrostatic energy is expressed as atomic multipole interactions between atom pairs, and many-body polarization is accounted for by mutually induced atomic dipoles. We have systematically investigated aromatic ring stacking and aromatic-water interactions for nucleobases and aromatic molecules, as well as base-base hydrogen-bonding pair interactions, all at various distances and orientations. van der Waals parameters were determined by comparison to the quantum mechanical interaction energy of these dimers and fine-tuned using condensed phase simulation. By comparing to quantum mechanical calculations, we show that the resulting classical potential is able to accurately describe molecular polarizability, molecular vibrational frequency, and dimer interaction energy of these aromatic systems. Condensed phase properties, including hydration free energy, liquid density, and heat of vaporization, are also in good overall agreement with experimental values. The structures of benzene liquid phase and benzene-water solution were also investigated by simulation and compared with experimental and PDB structure derived statistical results.

| S-EPMC5312700 | biostudies-literature

Force-Field Prediction of Materials Properties in Metal-Organic Frameworks.

Project description:In this work, MOF bulk properties are evaluated and compared using several force fields on several well-studied MOFs, including IRMOF-1 (MOF-5), IRMOF-10, HKUST-1, and UiO-66. It is found that, surprisingly, UFF and DREIDING provide good values for the bulk modulus and linear thermal expansion coefficients for these materials, excluding those that they are not parametrized for. Force fields developed specifically for MOFs including UFF4MOF, BTW-FF, and the DWES force field are also found to provide accurate values for these materials' properties. While we find that each force field offers a moderately good picture of these properties, noticeable deviations can be observed when looking at properties sensitive to framework vibrational modes. This observation is more pronounced upon the introduction of framework charges.

| S-EPMC5253710 | biostudies-literature

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE.

Project description:We demonstrate that fast and accurate linear force fields can be built for molecules using the atomic cluster expansion (ACE) framework. The ACE models parametrize the potential energy surface in terms of body-ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the four- or five-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine-learning-based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark data sets, but we also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal-mode prediction, high-temperature molecular dynamics, dihedral torsional profile prediction, and even bond breaking. We also demonstrate the smoothness, transferability, and extrapolation capabilities of ACE on a new challenging benchmark data set comprised of a potential energy surface of a flexible druglike molecule.

| S-EPMC8675139 | biostudies-literature

Development and Validation of a DFT-Based Force Field for a Hydrated Homoalanine Polypeptide.

Project description:A new force field has been created for simulating hydrated alanine polypeptides using the adaptive force matching (AFM) method. Only density functional theory calculations using the Perdew-Burke-Ernzerhof exchange-correlation functional and the D3 dispersion correction were used to fit the force field. The new force field, AFM2020, predicts NMR scalar coupling constants for hydrated homopolymeric alanine in better agreements with experimental data than several other models including those fitted directly to such data. For Ala7, the new force field shows about 15% helical conformations, 20% conformation in the β basin, and 65% polyproline II. The predicted helical population of short hydrated alanine is higher than previous estimates based on the same experimental data. Gas-phase simulations indicate that the force field developed by AFM solution-phase data is likely to produce a reasonable conformation distribution when hydration water is no longer present, such as the interior of a protein.

| S-EPMC7899179 | biostudies-literature

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

Project description:Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition. In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules. We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short. ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation. AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale. We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O. To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations. Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set.

| S-EPMC5414547 | biostudies-literature

Visualization and identification of single meteoritic organic molecules by atomic force microscopy.

Project description:Using high-resolution atomic force microscopy (AFM) with CO-functionalized tips, we atomically resolved individual molecules from Murchison meteorite samples. We analyzed powdered Murchison meteorite material directly, as well as processed extracts that we prepared to facilitate characterization by AFM. From the untreated Murchison sample, we resolved very few molecules, as the sample contained mostly small molecules that could not be identified by AFM. By contrast, using a procedure based on several trituration and extraction steps with organic solvents, we isolated a fraction enriched in larger organic compounds. The treatment increased the fraction of molecules that could be resolved by AFM, allowing us to identify organic constituents and molecular moieties, such as polycyclic aromatic hydrocarbons and aliphatic chains. The AFM measurements are complemented by high-resolution mass spectrometry analysis of Murchison fractions. We provide a proof of principle that AFM can be used to image and identify individual organic molecules from meteorites and propose a method for extracting and preparing meteorite samples for their investigation by AFM. We discuss the challenges and prospects of this approach to study extraterrestrial samples based on single-molecule identification.

| S-EPMC9305854 | biostudies-literature

Reproducing basic pKa values for turkey ovomucoid third domain using a polarizable force field.

Project description:We have extended our previous studies of calculating acidity constants for the acidic residues found in the turkey ovomucoid third domain protein (OMTKY3) by determining the relative pKa values for the basic residues (Lys13, Arg21, Lys29, Lys34, His52, and Lys55). A polarizable force field (PFF) was employed. The values of the pKa were found by direct comparison of energies of solvated protonated and deprotonated forms of the protein. Poisson-Boltzmann (PBF) and surface generalized Born (SGB) continuum solvation models represent the hydration, and a nonpolarizable fixed-charge OPLS-AA force field was used for comparison. Our results indicate that (i) the pKa values of the basic residues can be found in close agreement with the experimental values when a PFF is used in conjunction with the PBF solvation model, (ii) it is sufficient to take into the account only the residues which are in close proximity (hydrogen bonded) to the residue in question, and (iii) the PBF solvation model is superior to the SGB solvation model for these pKa calculations. The average error with the PBF/PFF model is only 0.7 pH unit, compared with 2.2 and 6.1 units for the PBF/OPLS and SGB/OPLS, respectively. The maximum deviation of the PBF/PFF results from the experimental values is 1.7 pH units compared with 6.0 pH units for the PBF/OPLS. Moreover, the best results were obtained while using an advanced nonpolar energy calculation scheme. The overall conclusion is that this methodology and force field are suitable for the accurate assessment of pKa shifts for both acidic and basic protein residues.

| S-EPMC2739454 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data