The prediction of the thermodynamic and kinetic properties of chemical reactions is increasingly being addressed by machine-learning (ML) methods, such as artificial neural networks (ANNs). While a number of recent studies have reported success in pr...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2022
After nearly two decades of research in the field of computational methods based on machine learning and knowledge-based potentials for ΔG and ΔΔG prediction upon variations, we now realize that all the approaches are poorly performing when tested on...
Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of re...
Understanding the permeation of biomolecules through cellular membranes is critical for many biotechnological applications, including targeted drug delivery, pathogen detection, and the development of new antibiotics. To this end, computer simulation...
Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free ...
Proceedings of the National Academy of Sciences of the United States of America
Mar 16, 2021
The protein design problem is to identify an amino acid sequence that folds to a desired structure. Given Anfinsen's thermodynamic hypothesis of folding, this can be recast as finding an amino acid sequence for which the desired structure is the lowe...
BACKGROUND: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can ...
Quantum mechanical predictive modelling in chemistry and biology is often hindered by the long time scales and large system sizes required of the computational model. Here, we employ the kernel regression machine learning technique to construct an an...
Molecular simulations are widely applied in the study of chemical and bio-physical problems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains chal...
Many natural substances and drugs are radical scavengers that prevent the oxidative damage to fundamental cell components. This process may occur via different mechanisms, among which, one of the most important, is hydrogen atom transfer. The feasibi...
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