Author

Yusuke Ihara

Monell Chemical Senses CenterORCID 0000-0002-1752-3698Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Neuroscience· 2

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 1 Approach:deep learning· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Joel D. MainlandiD· 2 shared Richard C. GerkiniD· 2 shared Alexei A. KoulakoviD· 1 shared Amit DhurandhariD· 1 shared Andreas KelleriD· 1 shared Bence SzalaiiD· 1 shared Celine VensiD· 1 shared Chung Wen YuiD· 1 shared

Activity by Year

2017-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersNeuroscience

Joel D. MainlandORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

NeurosciencebioRxivPreprintAug 8, 2025

A quantitative framework for predicting odor intensity across molecule and mixtures

Robert Pellegrino, Khristina V. Samoilova, Yusuke Ihara…·Joel D. Mainland

This study develops a deep learning framework to predict odor intensity from physical properties of molecules and mixtures, overcoming limitations of traditional methods.

6 citations

NeuroscienceSciencePeer-reviewedFeb 20, 2017

Reverse-engineering human olfactory perception from chemical features of odor molecules

Andreas Keller, Richard C. Gerkin, Yuanfang Guan…·Pablo Meyer

The paper demonstrates that machine learning can accurately predict odor perception from molecular features, enabling reverse-engineering of smell.

360 citationstop 1%

humanmachine learning (classical)