Iijima T - Search Results

1

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models.

Handa K, Sasaki S, Asano S, Kageyama M, Iijima T, Bender A. Handa K, et al. Among authors: iijima t. J Chem Inf Model. 2024 Sep 23;64(18):7068-7076. doi: 10.1021/acs.jcim.4c00921. Epub 2024 Sep 10. J Chem Inf Model. 2024. PMID: 39254593 Free PMC article.

2

QSAR model to predict K_p,uu,brain with a small dataset, incorporating predicted values of related parameter.

Umemori Y, Handa K, Sakamoto S, Kageyama M, Iijima T. Umemori Y, et al. Among authors: iijima t. SAR QSAR Environ Res. 2022 Nov;33(11):885-897. doi: 10.1080/1062936X.2022.2149619. SAR QSAR Environ Res. 2022. PMID: 36420623

3

Significance of Basal Membrane Permeability of Epithelial Cells in Predicting Intestinal Drug Absorption.

Yoshitomo A, Asano S, Hozuki S, Tamemoto Y, Shibata Y, Hashimoto N, Takahashi K, Sasaki Y, Ozawa N, Kageyama M, Iijima T, Kazuki Y, Sato H, Hisaka A. Yoshitomo A, et al. Among authors: iijima t. Drug Metab Dispos. 2023 Mar;51(3):318-328. doi: 10.1124/dmd.122.000907. Epub 2022 Dec 7. Drug Metab Dispos. 2023. PMID: 36810197

4

Prediction of Compound Plasma Concentration-Time Profiles in Mice Using Random Forest.

Handa K, Wright P, Yoshimura S, Kageyama M, Iijima T, Bender A. Handa K, et al. Among authors: iijima t. Mol Pharm. 2023 Jun 5;20(6):3060-3072. doi: 10.1021/acs.molpharmaceut.3c00071. Epub 2023 Apr 25. Mol Pharm. 2023. PMID: 37096989 Free PMC article.

5

Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors.

Handa K, Sakamoto S, Kageyama M, Iijima T. Handa K, et al. Among authors: iijima t. Eur J Drug Metab Pharmacokinet. 2023 Jul;48(4):341-352. doi: 10.1007/s13318-023-00832-w. Epub 2023 Jun 2. Eur J Drug Metab Pharmacokinet. 2023. PMID: 37266860

6

On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data.

Handa K, Thomas MC, Kageyama M, Iijima T, Bender A. Handa K, et al. Among authors: iijima t. J Cheminform. 2023 Nov 21;15(1):112. doi: 10.1186/s13321-023-00781-1. J Cheminform. 2023. PMID: 37990215 Free PMC article.

7

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Handa K, Yoshimura S, Kageyama M, Iijima T. Handa K, et al. Among authors: iijima t. J Chem Inf Model. 2024 May 13;64(9):3662-3669. doi: 10.1021/acs.jcim.4c00046. Epub 2024 Apr 19. J Chem Inf Model. 2024. PMID: 38639496

8

Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures.

Umemori Y, Handa K, Yoshimura S, Kageyama M, Iijima T. Umemori Y, et al. Among authors: iijima t. Biomolecules. 2024 Apr 30;14(5):535. doi: 10.3390/biom14050535. Biomolecules. 2024. PMID: 38785942 Free PMC article.

9

A Practical In Silico Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Handa K, Fujita D, Hirano M, Yoshimura S, Kageyama M, Iijima T. Handa K, et al. Among authors: iijima t. Mol Pharm. 2024 Oct 7;21(10):5182-5191. doi: 10.1021/acs.molpharmaceut.4c00584. Epub 2024 Sep 26. Mol Pharm. 2024. PMID: 39324316

10

An integrated population pharmacokinetic model of febuxostat in pediatric patients with hyperuricemia including gout and adult population of healthy subjects and patients with renal dysfunction.

Iwama R, Nishida K, Ishii D, Iijima T. Iwama R, et al. Among authors: iijima t. Pharmacol Res Perspect. 2024 Dec;12(6):e70032. doi: 10.1002/prp2.70032. Pharmacol Res Perspect. 2024. PMID: 39523739 Free PMC article.

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