関連する実験動画

Updated: Sep 9, 2025

Coulomb Explosion Imaging as a Tool to Distinguish Between Stereoisomers
08:51

Coulomb Explosion Imaging as a Tool to Distinguish Between Stereoisomers

Published on: August 18, 2017

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SMILESヘテロエンコーダから派生したキラリティ記述子の評価

Natalia Baimacheva1, Xinyue Gao2, Joao Aires-de-Sousa3

  • 1Faculty of Chemistry, University of Strasbourg, 4, Blaise Pascal Str., 67081, Strasbourg, France.

Journal of cheminformatics
|August 31, 2025
PubMed
まとめ

潜伏空間ベクトル (LSV) で訓練された機械学習モデルは,分子キラリティを予測する見込みを示した. 円形の指紋は,キラルエリューション順序を予測するためにLSVを上回ったが,差異のLSV記述者は予測精度を向上させた.

キーワード:
人工知能キラルHPLCキラリティ神経ネットワーク定量的構造と反選択性の関係ランダムな森林

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