Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge

Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom-typing or relatively expensive quantum mechanical methods to generate a density to be partitioned. Most machine learning efforts in this domain ignore total molecular charges, relying on overfitting and arbitrary rescaling in order to match the total system charge. Here we introduce the electron-passing neural network (EPNN), a fast, accurate neural network atomic charge partitioning model that conserves total molecular charge by construction. EPNNs predict atomic charges very similar to those obtained by partitioning quantum mechanical densities, but at such a small fraction of the cost that they can be easily computed for large biomolecules. Charges from this method may be used directly for molecular mechanics, as features for cheminformatics, or as input to any neural network potential.<br>

Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge

Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom-typing or relatively expensive quantum mechanical methods to generate a density to be partitioned. Most machine learning efforts in this domain ignore total molecular charges, relying on overfitting and arbitrary rescaling in order to match the total system charge. Here we introduce the electron-passing neural network (EPNN), a fast, accurate neural network atomic charge partitioning model that conserves total molecular charge by construction. EPNNs predict atomic charges very similar to those obtained by partitioning quantum mechanical densities, but at such a small fraction of the cost that they can be easily computed for large biomolecules. Charges from this method may be used directly for molecular mechanics, as features for cheminformatics, or as input to any neural network potential.<br>

DFT investigations on arylsulphonyl pyrazole derivatives as potential ligands of selected kinases

Using the density functional theory (DFT) formalism, we have investigated the properties of some arylsulphonyl indazole derivatives that we studied previously for their biological activity and susceptibility to interactions of azoles. This study includes the following physicochemical properties of these derivatives: electronegativity and polarisability (Mulliken charges, adjusted charge partitioning, and iterative-adjusted charge partitioning approaches); free energy of solvation (solvation model based on density model and M062X functional); highest occupied molecular orbital (HOMO)–lowest occupied molecular orbital (LUMO) gap together with the corresponding condensed Fukui functions, time-dependent DFT along with the UV spectra simulations using B3LYP, CAM-B3LYP, MPW1PW91, and WB97XD functionals, as well as linear response polarisable continuum model; and estimation of global chemical reactivity descriptors, particularly the chemical hardness factor. The charges on pyrrolic and pyridinic nitrogen (the latter one in the quinolone ring of compound 8, as well as condensed Fukui functions) reveal a significant role of these atoms in potential interactions of azole ligand–protein binding pocket. The lowest negative value of free energy of solvation can be attributed to carbazole 6, whereas pyrazole 7 has the least negative value of this energy. Moreover, the HOMO–LUMO gap and chemical hardness show that carbazole 6 and indole 5 exist as soft molecules, while fused pyrazole 7 has hard character.

Implications of the Fractional Charge of Hydroxide at the Electrochemical Interface

<div> <div> <div> <p>Rational design of materials that efficiently convert electrical energy into chemical bonds will ultimately depend on a thorough understanding of the electrochemical inter- face at the atomic level. Towards this goal, the use of density functional theory (DFT) at the generalized gradient approximation (GGA) level has been applied widely in the past 15 years. In the calculation of electrochemical reaction energetics using GGA-DFT, it is frequently implicitly assumed that ions in the Helmholtz plane have unit charge. However, the ion charge is observed to be fractional near the interface through both a capacitor model and through Bader charge partitioning. In this work, we show that this spurious charge transfer can be effectively mitigated by continuum charging of the electrolyte. We then show that, similar to hydronium, the observed fractional charge of hydroxide is not due to a GGA level self-interaction error, as the partial charge is observed even when using hybrid level exchange-correlation functionals. </p> </div> </div> </div>

Implications of the Fractional Charge of Hydroxide at the Electrochemical Interface

<div> <div> <div> <p>Rational design of materials that efficiently convert electrical energy into chemical bonds will ultimately depend on a thorough understanding of the electrochemical inter- face at the atomic level. Towards this goal, the use of density functional theory (DFT) at the generalized gradient approximation (GGA) level has been applied widely in the past 15 years. In the calculation of electrochemical reaction energetics using GGA-DFT, it is frequently implicitly assumed that ions in the Helmholtz plane have unit charge. However, the ion charge is observed to be fractional near the interface through both a capacitor model and through Bader charge partitioning. In this work, we show that this spurious charge transfer can be effectively mitigated by continuum charging of the electrolyte. We then show that, similar to hydronium, the observed fractional charge of hydroxide is not due to a GGA level self-interaction error, as the partial charge is observed even when using hybrid level exchange-correlation functionals. </p> </div> </div> </div>