activity
20222026
most citedDifficulty in chirality recognition for Transformer architectures learning chemical structures from string

42 citations · 77 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

From Syntax to Semantics: Unveiling the Emergence of Chirality in SMILES Translation Models

Zehao Li, Yasuhiro Yoshikai, Shumpei Nemoto +2

Understanding how chemical language models (CLMs) learn chemical meaning from molecular string representations, rather than only surface-level string patterns, is an important ques…

q-bio.QM2025★ 2 cited

Notation-level confounding: When inconsistent molecular notations mislead chemical language models

Yosuke Kikuchi, Yasuhiro Yoshikai, Shumpei Nemoto +4

Chemical language models (CLMs) are increasingly used for molecular design and property prediction. Because these models learn from textual encodings of molecules, differences in h…

q-bio.BM2024★ 5 cited

A novel molecule generative model of VAE combined with Transformer for unseen structure generation

Yasuhiro Yoshikai, Tadahaya Mizuno, Shumpei Nemoto +1

Recently, molecule generation using deep learning has been actively investigated in drug discovery. In this field, Transformer and VAE are widely used as powerful models, but they…

cs.LG2023★ 42 cited

Difficulty in chirality recognition for Transformer architectures learning chemical structures from string

Yasuhiro Yoshikai, Tadahaya Mizuno, Shumpei Nemoto +1

Recent years have seen rapid development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language proc…

physics.chem-ph2022★ 14 cited

Investigation of chemical structure recognition by encoder-decoder models in learning progress

Shumpei Nemoto, Tadahaya Mizuno, Hiroyuki Kusuhara

Descriptor generation methods using latent representations of encoderdecoder (ED) models with SMILES as input are useful because of the continuity of descriptor and restorabilit…

cs.LG2022★ 14 cited

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning

Katsuhisa Morita, Tadahaya Mizuno, Hiroyuki Kusuhara

Adverse events are a serious issue in drug development and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto sta…