A fast empirical GAFF compatible partial atomic charge assignment scheme for modeling interactions of small molecules with biomolecular targets

2010 ◽  
Vol 32 (5) ◽  
pp. 893-907 ◽  
Author(s):  
Goutam Mukherjee ◽  
Niladri Patra ◽  
Poranjyoti Barua ◽  
B. Jayaram
Keyword(s):  
Small Molecules ◽  
Atomic Charge ◽  
Partial Atomic Charge ◽  
Charge Assignment ◽  
Modeling Interactions ◽  
Assignment Scheme

scholarly journals Automated partial atomic charge assignment for drug-like molecules: a fast knapsack approach

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Martin S. Engler ◽  
Bertrand Caron ◽  
Lourens Veen ◽  
Daan P. Geerke ◽  
Alan E. Mark ◽  
...  
Keyword(s):  
Atomic Charge ◽  
Partial Atomic Charge ◽  
Charge Assignment

Parameterization of empirical schemes of partial atomic charge calculation for reproducing the molecular electrostatic potential

2008 ◽  
Vol 419 (1) ◽  
pp. 57-61 ◽  
Author(s):  
D. A. Shulga ◽  
A. A. Oliferenko ◽  
S. A. Pisarev ◽  
V. A. Palyulin ◽  
N. S. Zefirov
Keyword(s):  
Electrostatic Potential ◽  
Molecular Electrostatic Potential ◽  
Atomic Charge ◽  
Partial Atomic Charge

Fast and accurate prediction of partial charges using Atom-Path-Descriptor-based machine learning

2020 ◽  
Vol 36 (18) ◽  
pp. 4721-4728
Author(s):  
Jike Wang ◽  
Dongsheng Cao ◽  
Cunchen Tang ◽  
Xi Chen ◽  
Huiyong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
High Accuracy ◽  
Free Energy Calculations ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Partial Charge ◽  
Charge Assignment ◽  
Partial Charges ◽  
Extreme Gradient Boosting

Abstract Motivation Partial atomic charges are usually used to calculate the electrostatic component of energy in many molecular modeling applications, such as molecular docking, molecular dynamics simulations, free energy calculations and so forth. High-level quantum mechanics calculations may provide the most accurate way to estimate the partial charges for small molecules, but they are too time-consuming to be used to process a large number of molecules for high throughput virtual screening. Results We proposed a new molecule descriptor named Atom-Path-Descriptor (APD) and developed a set of APD-based machine learning (ML) models to predict the partial charges for small molecules with high accuracy. In the APD algorithm, the 3D structures of molecules were assigned with atom centers and atom-pair path-based atom layers to characterize the local chemical environments of atoms. Then, based on the APDs, two representative ensemble ML algorithms, i.e. random forest (RF) and extreme gradient boosting (XGBoost), were employed to develop the regression models for partial charge assignment. The results illustrate that the RF models based on APDs give better predictions for all the atom types than those based on traditional molecular fingerprints reported in the previous study. More encouragingly, the models trained by XGBoost can improve the predictions of partial charges further, and they can achieve the average root-mean-square error 0.0116 e on the external test set, which is much lower than that (0.0195 e) reported in the previous study, suggesting that the proposed algorithm is quite promising to be used in partial charge assignment with high accuracy. Availability and implementation The software framework described in this paper is freely available at https://github.com/jkwang93/Atom-Path-Descriptor-based-machine-learning Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Capturing non-local through-bond effects when fragmenting molecules for quantum chemical torsion scans

2020 ◽  
Author(s):  
Chaya D Stern ◽  
Christopher I Bayly ◽  
Daniel G A Smith ◽  
Josh Fass ◽  
Lee-Ping Wang ◽  
...  
Keyword(s):  
Small Molecules ◽  
Quantum Chemical ◽  
Fragment Size ◽  
Computational Cost ◽  
Molecular Size ◽  
Intramolecular Interactions ◽  
Chemical Environment ◽  
Biomolecular Systems ◽  
Charge Assignment ◽  
Torsion Energy

AbstractAccurate molecular mechanics force fields for small molecules are essential for predicting protein-ligand binding affinities in drug discovery and understanding the biophysics of biomolecular systems. Torsion potentials derived from quantum chemical (QC) calculations are critical for determining the conformational distributions of small molecules, but are computationally expensive and scale poorly with molecular size. To reduce computational cost and avoid the complications of distal through-space intramolecular interactions, molecules are generally fragmented into smaller entities to carry out QC torsion scans. However, torsion potentials, particularly for conjugated bonds, can be strongly affected by through-bond chemistry distal to the torsion itself. Poor fragmentation schemes have the potential to significantly disrupt electronic properties in the region around the torsion by removing important, distal chemistries, leading to poor representation of the parent molecule’s chemical environment and the resulting torsion energy profile. Here we show that a rapidly computable quantity, the fractional Wiberg bond order (WBO), is a sensitive reporter on whether the chemical environment around a torsion has been disrupted. We show that the WBO can be used as a surrogate to assess the robustness of fragmentation schemes and identify conjugated bond sets. We use this concept to construct a validation set by exhaustively fragmenting a set of druglike organic molecules and examine their corresponding WBO distributions derived from accessible conformations that can be used to evaluate fragmentation schemes. To illustrate the utility of the WBO in assessing fragmentation schemes that preserve the chemical environment, we propose a new fragmentation scheme that uses rapidly-computable AM1 WBOs, which are available essentially for free as part of standard AM1-BCC partial charge assignment. This approach can simultaneously maximize the chemical equivalency of the fragment and the substructure in the larger molecule while minimizing fragment size to accelerate QC torsion potential computation for small molecules and reducing undesired through-space steric interactions.

scholarly journals Discovering a Transferable Charge Assignment Model Using Machine Learning

2018 ◽  
Author(s):  
Andrew E. Sifain ◽  
Nicholas Lubbers ◽  
Benjamin T. Nebgen ◽  
Justin S. Smith ◽  
Andrey Y. Lokhov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dipole Moments ◽  
Computational Cost ◽  
Atomic Charge ◽  
Ground Truth ◽  
Quadrupole Moments ◽  
Molecular Conformations ◽  
Charge Assignment ◽  
Charge Model ◽  
A Charge

<p>Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics. Machine learning has emerged as a powerful tool for modeling chemistry at unprecedented computational speeds given ground-truth values, but for the task of charge assignment, the choice of ground-truth may not be obvious. In this letter, we use machine learning to discover a charge model by training a neural network to molecular dipole moments using a large, diverse set of CHNO molecular conformations. The new model, called Affordable Charge Assignment (ACA), is computationally inexpensive and predicts dipoles of out-of-sample molecules accurately. Furthermore, dipole-inferred ACA charges are transferable to dipole and even quadrupole moments of much larger molecules than those used for training. We apply ACA to long dynamical trajectories of biomolecules and successfully produce their infrared spectra. Additionally, we compare ACA with existing charge models and find that ACA assigns similar charges to Charge Model 5, but with a greatly reduced computational cost.</p>

scholarly journals Discovering a Transferable Charge Assignment Model Using Machine Learning

2018 ◽  
Author(s):  
Andrew E. Sifain ◽  
Nicholas Lubbers ◽  
Benjamin T. Nebgen ◽  
Justin S. Smith ◽  
Andrey Y. Lokhov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dipole Moments ◽  
Computational Cost ◽  
Atomic Charge ◽  
Ground Truth ◽  
Quadrupole Moments ◽  
Molecular Conformations ◽  
Charge Assignment ◽  
Charge Model ◽  
A Charge

<p>Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics. Machine learning has emerged as a powerful tool for modeling chemistry at unprecedented computational speeds given ground-truth values, but for the task of charge assignment, the choice of ground-truth may not be obvious. In this letter, we use machine learning to discover a charge model by training a neural network to molecular dipole moments using a large, diverse set of CHNO molecular conformations. The new model, called Affordable Charge Assignment (ACA), is computationally inexpensive and predicts dipoles of out-of-sample molecules accurately. Furthermore, dipole-inferred ACA charges are transferable to dipole and even quadrupole moments of much larger molecules than those used for training. We apply ACA to long dynamical trajectories of biomolecules and successfully produce their infrared spectra. Additionally, we compare ACA with existing charge models and find that ACA assigns similar charges to Charge Model 5, but with a greatly reduced computational cost.</p>

Sign in / Sign up

Export Citation Format

Share Document