Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted...
Accurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been s...
Structure-based virtual screening (SBVS) via docking has been used to discover active molecules for a range of therapeutic targets. Chemical and protein data sets that contain integrated bioactivity information have increased both in number and in si...
SLAS discovery : advancing life sciences R & D
Oct 14, 2023
MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzin...
Journal of biomolecular structure & dynamics
Oct 12, 2023
The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics incr...
Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been pro...
Structure-based virtual screening plays a critical role in drug discovery. However, numerous docking programs, such as AutoDock Vina and Glide, are time-consuming due to the necessity of generating numerous molecular conformations and executing steps...
Ligand docking is one of the core technologies in structure-based virtual screening for drug discovery. However, conventional docking tools and existing deep learning tools may suffer from limited performance in terms of speed, pose quality and bindi...
Physical chemistry chemical physics : PCCP
Sep 13, 2023
Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of prote...
Journal of chemical information and modeling
Sep 11, 2023
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared with traditio...