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Found 14 papers, 10 papers with code
PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
As the size of accessible compound libraries expands to over 10 billion, the need for more efficient structure-based virtual screening methods is emerging.
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.
Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs
The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics.
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.
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.
Uncertainty quantification of molecular property prediction with Bayesian neural networks
Deep neural networks have outperformed existing machine learning models in various molecular applications.
Uncertainty quantification of molecular property prediction using Bayesian neural network models
In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions.
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.
