Yoshihiro Uesawa

Professor

Meiji Pharmaceutical University

Japan

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Yoshihiro Uesawa is a Professor at Meiji Pharmaceutical University in Japan. His research focuses on pharmaceutical toxicity prediction, computational toxicology, and drug safety, as evidenced by his recent articles on adverse drug reactions and predictive models for drug screening. He has also conducted comprehensive studies on drug-induced reactions, including pruritus and liver injury, using various pharmacovigilance databases. Additionally, his work explores molecular descriptors and deep learning techniques in the context of drug development and evaluation.

Yoshihiro Uesawa

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Articles (20)

Efficiency of pharmaceutical toxicity prediction in computational toxicology

A Comparative Study of Tumor-Specificity and Neurotoxicity between 3-Styrylchromones and Anti-Cancer Drugs

Analysis of Opioid-Related Adverse Events in Japan Using FAERS Database

Comprehensive Study of Drug-Induced Pruritus Based on Adverse Drug Reaction Report Database

Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening

Analysis of Corticosteroid-Induced Glaucoma Using the Japanese Adverse Drug Event Reporting Database

Identifying Crude Drugs in Kampo Medicines Associated with Drug-Induced Liver Injury Using the Japanese Adverse Drug Event Report Database: A Comprehensive Survey

Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning

Examination of Risk Factors and Expression Patterns of Atypical Femoral Fractures Using the Japanese Adverse Drug Event Report Database: A Retrospective Pharmacovigilance Study

Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure–Activity Relationships

Tumor-Specificity, Neurotoxicity, and Possible Involvement of the Nuclear Receptor Response Pathway of 4,6,8-Trimethyl Azulene Amide Derivatives

Investigation of carbonic anhydrase inhibitory effects and cytotoxicities of pyrazole-based hybrids carrying hydrazone and zinc-binding benzenesulfonamide pharmacophores

Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures

Comprehensive Analysis of Adverse Events Induced by PARP Inhibitors Using JADER and Time to Onset

Trends in reporting embolic and thrombotic events after COVID-19 vaccination: A retrospective, pharmacovigilance study

Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning

Time to Onset of Bendamustine-associated Skin Damage Using the Spontaneous Reporting System.

Comprehensive analysis of everolimus-induced adverse events using the Japanese real-world database.

A Deep Learning-Based Quantitative Structure–Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

Inhibition of Neurotoxicity/Anticancer Activity of Bortezomib by Caffeic Acid and Chlorogenic Acid.

Collaborators (6)

Hiroshi Sakagami

Tadashi Shimizu

Kenjiro Bandow

Akira Sato

Keitaro Satoh

Masashi HASHIMOTO

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