Author

Yutaka Akiyama

Institute of Science TokyoORCID 0000-0003-2863-8703Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1

Frequent Co-Authors

Keisuke YanagisawaiD· 3 shared Jianan LiiD· 2 shared Masatake SugitaiD· 1 shared Masayoshi ShimizuiD· 1 shared Satoshi Yoneyama· 1 shared Takuya FujieiD· 1 shared Yudai Noso· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 13 mutual co-author links

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3 shared papersBioinformatics

Keisuke YanagisawaORCID

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3 papers

Recent·Most cited

BiophysicsACS OmegaPeer-reviewedJun 30, 2026

Protocol for Membrane Permeability Prediction of Cyclic Peptides using Descriptors Obtained from Extended Ensemble Molecular Dynamics Simulations and Chemical Structures

Masatake Sugita, Yudai Noso, Jianan Li…·Yutaka Akiyama

The paper develops a machine learning protocol to predict cyclic peptide membrane permeability using 3D descriptors from MD simulations and 2D chemical descriptors, achieving R = 0.77 and RMSE = 0.62 on training data.

2 citationstop 5%

proteomicsmachine learning (classical)

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BioinformaticsJournal of Chemical Information and ModelingPeer-reviewedApr 6, 2026

COFFEE-PRESC: A fast pre-screening method using compound retrieval by pairwise positional relationship of representative fragments

Masayoshi Shimizu, Satoshi Yoneyama, Keisuke Yanagisawa, Yutaka Akiyama

COFFEE-PRESC is a fast pre-screening method that uses fragment pairs to efficiently filter large compound libraries with higher accuracy than existing tools.

DOI 10.1021/acs.jcim.5c03067

experimental assaydrug response / drug discovery

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BioinformaticsBriefings in BioinformaticsPeer-reviewedJul 25, 2024

CycPeptMP: Enhancing Membrane Permeability Prediction of Cyclic Peptides with Multi-Level Molecular Features and Data Augmentation

Jianan Li, Keisuke Yanagisawa, Yutaka Akiyama

CycPeptMP predicts cyclic peptide membrane permeability using multi-level features and data augmentation, achieving high accuracy with RMSE 0.503 and correlation 0.883.

39 citationstop 1%

deep learning

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