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Accurate Free Energy Estimations of Molecular Systems Via Flow-based Targeted Free Energy Perturbation
The Targeted Free Energy Perturbation (TFEP) method aims to overcome the time-consuming and computer-intensive stratification process of standard methods for estimating the free energy difference between two states.
Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks
The model generated compounds which are structurally different form the training set, while also being more similar to compounds known to bind to the two families of drug targets compared to a random set of molecules.
Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix
Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design.
On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning
These molecular interaction fields are mapped to the corresponding 3D image of hydration occupancy using a neural network based on an U-Net architecture.
