Molecular Dynamics and Quantum Chemical Approach for the Estimation of an Intramolecular Hydrogen Bond Strength in Okadaic Acid

We have evaluated the strength of intramolecular hydrogen bond in a protein based on molecular dynamics and quantum chemical calculation. To estimate the intramolecular hydrogen bond strength in okadaic acid (OA), we analyzed the influence of solvent and protonation states on the hydrogen bond and the entire structure. We performed molecular dynamics calculation and analyzed the strength of the hydrogen bond by measuring bond length and bond angle. The stable structure differs depending on the kind of solvent used and the protonation state of OA. Using the mean interaction energy from the quantum chemical calculation, hydrogen bond length and angle were investigated against bond energy. Although dielectric constant slightly depends on bond energy, the estimation of the intramolecular hydrogen bond strength in OA is possible even in a protein environment. The Coulomb interaction between OA and surrounding arginine produced a more negatively charged O1 in OA. The hydrogen bond energy in the deprotonated state is larger than that in the protonated state

Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules

Density functional theory (DFT) is a significant computational tool that has substantially influenced chemistry, physics, and materials science. DFT necessitates parametrized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule. Herein, we optimize the parameters of range-separated functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO) to satisfy Koopmans’ theorem. Our results demonstrate the effectiveness of the BO in optimizing functional parameters. Particularly, Koopmans’ theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to the experimental UV-absorption values. Furthermore, we prepared an optimized parameter dataset of KTLC-BLYP for over 3000 molecules through BO for satisfying Koopmans’ theorem. We have developed a machine learning model on this dataset to predict the parameters of the LC-BLYP functional for a given molecule. The prediction model automatically predicts the appropriate parameters for a given molecule and calculates the corresponding values. The approach in this paper would be useful to develop new functionals and to update the previously developed functionals

Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules

Density functional theory (DFT) is a significant computational tool that has substantially influenced chemistry, physics, and materials science. DFT necessitates parametrized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule. Herein, we optimize the parameters of range-separated functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO) to satisfy Koopmans’ theorem. Our results demonstrate the effectiveness of the BO in optimizing functional parameters. Particularly, Koopmans’ theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to the experimental UV-absorption values. Furthermore, we prepared an optimized parameter dataset of KTLC-BLYP for over 3000 molecules through BO for satisfying Koopmans’ theorem. We have developed a machine learning model on this dataset to predict the parameters of the LC-BLYP functional for a given molecule. The prediction model automatically predicts the appropriate parameters for a given molecule and calculates the corresponding values. The approach in this paper would be useful to develop new functionals and to update the previously developed functionals