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Found 5 papers, 2 papers with code
Learning Collective Variables with Synthetic Data Augmentation through Physics-inspired Geodesic Interpolation
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs.
Chemically Transferable Generative Backmapping of Coarse-Grained Proteins
Coarse-graining (CG) accelerates molecular simulations of protein dynamics by simulating sets of atoms as singular beads.
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation
We further show with ablation studies that our method, predictive error-PER (FREED(PE)), significantly improves the model performance.
PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions
Recently, deep neural network (DNN)-based drug-target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs.
A comprehensive study on the prediction reliability of graph neural networks for virtual screening
Prediction models based on deep neural networks are increasingly gaining attention for fast and accurate virtual screening systems.
