奥野恭史
奥野, 恭史
Okuno, Yasushi
VIAF ID: 254874530 (Personal)
Permalink: http://viaf.org/viaf/254874530
Preferred Forms
- 100 1 _ ‡a Okuno, Yasushi
- 100 1 _ ‡a 奥野, 恭史
- 100 1 _ ‡a 奥野, 恭史
- 100 1 _ ‡a 奥野, 恭史
- 100 0 _ ‡a 奥野恭史
4xx's: Alternate Name Forms (8)
Works
Title | Sources |
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Accurate Prediction of Complex Structure and Affinity for a Flexible Protein Receptor and Its Inhibitor. | |
Association between homologous recombination repair gene mutations and response to oxaliplatin in pancreatic cancer. | |
Calculation of absolute binding free energies between the hERG channel and structurally diverse drugs | |
CGBVS-DNN: Prediction of Compound-protein Interactions Based on Deep Learning | |
Clinical sequencing using a next-generation sequencing-based multiplex gene assay in patients with advanced solid tumors. | |
Coarse-grained diffraction template matching model to retrieve multi-conformational models for biomolecule structures from noisy diffraction patterns | |
Constructing a Foundational Platform Driven by Japan's K Supercomputer for Next-Generation Drug Design | |
Core Binding Site of a Thioflavin-T-Derived Imaging Probe on Amyloid β Fibrils Predicted by Computational Methods. | |
Drug Development Value Chain Constructed by Collaboration Between The SOSHO Project and The NPO BIOGRID | |
Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking | |
Exome Sequencing Landscape Analysis in Ovarian Clear Cell Carcinoma Shed Light on Key Chromosomal Regions and Mutation Gene Networks. | |
Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning | |
Fibronectin type III domain-containing protein 5 interacts with APP and decreases amyloid β production in Alzheimer's disease | |
Force-field parametrization based on radial and energy distribution functions | |
High-precision Atomic Charge Prediction for Protein Systems Using Fragment Molecular Orbital Calculation and Machine Learning | |
Identification of a new class of non-electrophilic TRPA1 agonists by a structure-based virtual screening approach | |
Improving the Accuracy of Protein-Ligand Binding Mode Prediction Using a Molecular Dynamics-Based Pocket Generation Approach | |
An in silico Approach for Integrating Phenotypic and Target-based Approaches in Drug Discovery | |
In silico drug discovery by supercomputer "K". | |
Inshiriko sōyaku kagaku : Genomu jōhō kara sōyaku e | |
kGCN: A Graph-Based Deep Learning Framework for Chemical Structures | |
Landscape and function of multiple mutations within individual oncogenes | |
Machine Learning Accelerates MD-based Binding-Pose Prediction between Ligands and Proteins | |
MGeND: an integrated database for Japanese clinical and genomic information | |
Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare mutations | |
New progress in crystallization technology of membrane protein and introduction of pharamaceutical innovation value chain | |
A Platform for Comprehensive Genomic Profiling in Human Cancers and Pharmacogenomics Therapy Selection | |
Prediction and Interpretable Visualization of Synthetic Reactions Using Graph Convolutional Networks | |
Saishin sōyaku infomatikusu katsuyō manyuaru, 2011: | |
A secondary RET mutation in the activation loop conferring resistance to vandetanib. | |
Stabilization Mechanism for a Nonfibrillar Amyloid β Oligomer Based on Formation of a Hydrophobic Core Determined by Dissipative Particle Dynamics | |
Structural modification of indomethacin toward selective inhibition of COX-2 with a significant increase in van der Waals contributions | |
インシリコ創薬科学 : ゲノム情報から創薬へ | |
最新創薬インフォマティクス活用マニュアル |