Search Results for author:
Found 10 papers, 8 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.
C3Net: interatomic potential neural network for prediction of physicochemical properties in heterogenous systems
Understanding the interactions of a solute with its environment is of fundamental importance in chemistry and biology.
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.
Fragment-based molecular generative model with high generalization ability and synthetic accessibility
The high synthetic accessibility of the generated molecules is implicitly considered while preparing the fragment library with the BRICS decomposition method.
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.
Molecular Generative Model Based On Adversarially Regularized Autoencoder
All models reported so far are based on either variational autoencoder (VAE) or generative adversarial network (GAN).
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
Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design.
Molecular generative model based on conditional variational autoencoder for de novo molecular design
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design.
Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery.
