7 citations · 20 across the 8 of their papers we have counts for
8 papers
Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction
Apakorn Kengkanna, Masahito Ohue
Graph neural networks (GNNs) demonstrate great performance in compound property and activity prediction due to their capability to efficiently learn complex molecular graph structu…
Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy Perturbation
Kairi Furui, Masahito Ohue
In recent years, free energy perturbation (FEP) calculations have garnered increasing attention as tools to support drug discovery. The lead optimization mapper (Lomap) was propose…
Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain
Kairi Furui, Masahito Ohue
Learning-to-rank, a machine learning technique widely used in information retrieval, has recently been applied to the problem of ligand-based virtual screening, to accelerate the e…
MEGADOCK-GUI: a GUI-based complete cross-docking tool for exploring protein-protein interactions
Masahito Ohue, Yutaka Akiyama
Information on protein-protein interactions (PPIs) not only advances our understanding of molecular biology but also provides important clues for target selection in drug discovery…
MEGADOCK-Web-Mito: human mitochondrial protein-protein interaction prediction database
Masahito Ohue, Hiroki Watanabe, Yutaka Akiyama
Mitochondrial diseases are largely caused by dysfunction in mitochondrial proteins. However, annotations of human mitochondrial proteins are scattered across various public databas…
High-performance cloud computing for exhaustive protein-protein docking
Masahito Ohue, Kento Aoyama, Yutaka Akiyama
Public cloud computing environments, such as Amazon AWS, Microsoft Azure, and the Google Cloud Platform, have achieved remarkable improvements in computational performance in recen…
Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph
Masahito Ohue, Ryota Ii, Keisuke Yanagisawa +1
Machine learning is often used in virtual screening to find compounds that are pharmacologically active on a target protein. The weave module is a type of graph convolutional deep…
Link Mining for Kernel-based Compound-Protein Interaction Predictions Using a Chemogenomics Approach
Masahito Ohue, Takuro Yamazaki, Tomohiro Ban +1
Virtual screening (VS) is widely used during computational drug discovery to reduce costs. Chemogenomics-based virtual screening (CGBVS) can be used to predict new compound-protein…