Moena Kagaya, Yoshihiro Uesawa. Nuclear Receptors and Stress Response Pathways Associated with the Development of Oral Mucositis Induced by Antineoplastic Agents. Pharmaceuticals (Basel, Switzerland). 2024. 17. 8
Ryuichiro Hosoya, Reiko Ishii-Nozawa, Tomoko Terajima, Hajime Kagaya, Yoshihiro Uesawa. The Association between Molecular Initiating Events and Drug-Induced Hiccups. Pharmaceuticals. 2024. 17. 3. 379-379
Yoshihiro Uesawa. Progress in Predicting Ames Test Outcomes from Chemical Structures: An In-Depth Re-Evaluation of Models from the 1st and 2nd Ames/QSAR International Challenge Projects. International journal of molecular sciences. 2024. 25. 3
Cheminformatics, QSAR, and Machine Learning Applications for Novel Drug Development
ELSEVIER 2023
ケモインフォマティクスデータ収集の最適化と解析手法
2023
医療ビッグデータ、人工知能がもたらす創薬研究の未来像
NTS 2022
Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis
Frontiers eBook 2020
QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer, Biological Efficacy of Natural and Chemically Modified Products against Oral Inflammatory Lesions
MDPI Books 2019
2023/01 - Biological and Pharmaceutical Bulletin誌 Editor's pick:Featured article Impact of Drugs and Patient Characteristics on Life Expectancy during the Induction Phase of Dialysis
2022/04 - 日本薬学会第142年会 学生優秀発表賞
2022/04 - 日本薬学会第142年会 学生優秀発表賞
2021 - Chem. Pharm. Bull. Highlighted paper selected by Editor-in-Chief Use of 13C-NMR chemical shifts; Application of principal component analysis for categorizing structurally similar methoxyflavones and correlation analysis between chemical shifts and cytotoxicity
2016 - International Institute of Anticancer Research (IIAR) 'Exceptional Quality Paper'(優秀論文) Quantitative Structure-cytotoxicity Relationship of 3-Benzylidenechromanones
2009 - ICANN'09: International Conference on Artificial Neural Networks, European Neural Network Society (ENNS) and CADASTER project Environmental Toxicity Prediction Challenge First Pass Winner
CBI学会 ポスター賞(Excellent Poster) Quantitative Structure-Activity Relationship (QSAR) analysis using deep learning based on deep snap, a novel molecular image input technique
全件表示
所属学会 (14件):
Sigma Xi
, 日本毒性学会
, 日本医薬品安全性学会
, 日本緩和医療薬学会
, 日本薬剤学会
, 日本医療薬学会
, 日本薬物動態学会
, 日本薬理学会
, 日本化学会情報化学部会
, 情報計算化学生物学会
, 日本臨床薬理学会
, 日本薬学会
, The Japanese Pharmacological Society
, Japanese Society for the Study of Xenobiotics