Search Results for author:
Found 5 papers, 3 papers with code
DeepBioisostere: Discovering Bioisosteres with Deep Learning for a Fine Control of Multiple Molecular Properties
Our model's innovation lies in its capacity to design a bioisosteric replacement reflecting the compatibility with the surroundings of the modification site, facilitating the control of sophisticated properties like drug-likeness.
PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening
Prediction of protein-ligand interactions (PLI) plays a crucial role in drug discovery as it guides the identification and optimization of molecules that effectively bind to target proteins.
GeoTMI:Predicting quantum chemical property with easy-to-obtain geometry via positional denoising
Hence, to incorporate information of the correct, GeoTMI aims to maximize mutual information between three variables: the correct and the corrupted geometries and the property.
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.
Scaffold-based molecular design using graph generative model
Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest.
Ranked #2 on
Molecular Graph Generation
on InterBioScreen
