Graph-based molecular representation learning
Molecular representation learning (MRL) is a key step to build the connection between
machine learning and chemical science. In particular, it encodes molecules as numerical …
machine learning and chemical science. In particular, it encodes molecules as numerical …
Deep learning methods for small molecule drug discovery: a survey
With the development of computer-assisted techniques, research communities, including
biochemistry and deep learning, have been devoted into the drug discovery field for over a …
biochemistry and deep learning, have been devoted into the drug discovery field for over a …
DockStream: a docking wrapper to enhance de novo molecular design
Recently, we have released the de novo design platform REINVENT in version 2.0. This
improved and extended iteration supports far more features and scoring function …
improved and extended iteration supports far more features and scoring function …
3D-scaffold: a deep learning framework to generate 3D coordinates of drug-like molecules with desired scaffolds
The prerequisite of therapeutic drug design and discovery is to identify novel molecules and
developing lead candidates with desired biophysical and biochemical properties. Deep …
developing lead candidates with desired biophysical and biochemical properties. Deep …
A mobility aware network traffic prediction model based on dynamic graph attention spatio-temporal network
Z Jin, J Qian, Z Kong, C Pan - Computer Networks, 2023 - Elsevier
Network traffic prediction is a critical research topic in network management and planning.
Due to the growing service requirements and diverse service types, network traffic exhibits …
Due to the growing service requirements and diverse service types, network traffic exhibits …
Differentiable Clustering for Graph Attention
Graph clusters (or communities) represent important graph structural information. In this
paper, we present D ifferentiable C lustering for graph AT tention (DCAT). To the best of our …
paper, we present D ifferentiable C lustering for graph AT tention (DCAT). To the best of our …
Deep surrogate docking: Accelerating automated drug discovery with graph neural networks
The process of screening molecules for desirable properties is a key step in several
applications, ranging from drug discovery to material design. During the process of drug …
applications, ranging from drug discovery to material design. During the process of drug …
3d-transformer: Molecular representation with transformer in 3d space
Spatial structures in the 3D space are important to determine molecular properties. Recent
papers use geometric deep learning to represent molecules and predict properties. These …
papers use geometric deep learning to represent molecules and predict properties. These …
Predicting cellular responses with variational causal inference and refined relational information
Predicting the responses of a cell under perturbations may bring important benefits to drug
discovery and personalized therapeutics. In this work, we propose a novel graph variational …
discovery and personalized therapeutics. In this work, we propose a novel graph variational …
[图书][B] Artificial intelligence and high-performance computing for accelerating structure-based drug discovery
AR Clyde - 2022 - search.proquest.com
Traditional techniques for discovering novel drugs are too slow for 21st challenges, from
precision oncology to emerging global pandemics. The COVID-19 Pandemic demonstrated …
precision oncology to emerging global pandemics. The COVID-19 Pandemic demonstrated …