Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
Graph neural networks for molecules
Graph neural networks (GNNs), which are capable of learning representations from
graphical data, are naturally suitable for modeling molecular systems. This review …
graphical data, are naturally suitable for modeling molecular systems. This review …
Ai-accelerated design of targeted covalent inhibitors for SARS-CoV-2
Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-
CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of …
CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of …
Graph Representation Learning for Interactive Biomolecule Systems
Advances in deep learning models have revolutionized the study of biomolecule systems
and their mechanisms. Graph representation learning, in particular, is important for …
and their mechanisms. Graph representation learning, in particular, is important for …
Mechanistic investigation of SARS-CoV-2 main protease to accelerate design of covalent inhibitors
Targeted covalent inhibition represents one possible strategy to block the function of SARS-
CoV-2 Main Protease (MPRO), an enzyme that plays a critical role in the replication of the …
CoV-2 Main Protease (MPRO), an enzyme that plays a critical role in the replication of the …
Prediction of the tetramer protein complex interaction based on CNN and SVM
Y Lyu, R He, J Hu, C Wang, X Gong - Frontiers in Genetics, 2023 - frontiersin.org
Protein-protein interactions play an important role in life activities. The study of protein-
protein interactions helps to better understand the mechanism of protein complex …
protein interactions helps to better understand the mechanism of protein complex …
De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning
Efficient design and discovery of target-driven molecules is a critical step in facilitating lead
optimization in drug discovery. Current approaches to develop molecules for a target protein …
optimization in drug discovery. Current approaches to develop molecules for a target protein …
Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction
The protein–ligand binding free energy is a central quantity in structure-based
computational drug discovery efforts. Although popular alchemical methods provide sound …
computational drug discovery efforts. Although popular alchemical methods provide sound …
Prediction of protein mononucleotide binding sites using AlphaFold2 and machine learning
S Yamaguchi, H Nakashima, Y Moriwaki… - … Biology and Chemistry, 2022 - Elsevier
In this study, we developed a system that predicts the binding sites of proteins for five
mononucleotides (AMP, ADP, ATP, GDP, and GTP). The system comprises two machine …
mononucleotides (AMP, ADP, ATP, GDP, and GTP). The system comprises two machine …
Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease
Identification of potential therapeutic candidates can be expedited by integrating
computational modeling with domain aware machine learning (ML) models followed by …
computational modeling with domain aware machine learning (ML) models followed by …