"機械学習によるC-Nクロスカップリングにおける反応性能の予測"に関するコメント
Kangway V Chuang1, Michael J Keiser2
1Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases, and Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA.
Science (New York, N.Y.)
|November 17, 2018
まとめ
C-Nクロスカップリング反応の産出量を予測する機械学習モデルが評価された. この研究では,実験的設計がモデルを検証するのに不十分であり,従来の機械学習制御では失敗していることが判明しました.
科学分野:
- 化学について
- 機械学習
- データサイエンス
背景:
- 化学反応の結果を予測することは 合成化学にとって極めて重要です
- マシン・ラーニング (ML) は,反応率を予測する可能性を秘めています.
- 化学における信頼性の高いMLアプリケーションには,正確なモデルの検証が不可欠です.
研究 の 目的:
- C-Nクロスカップリング反応を予測するMLモデルの有効性を評価する.
- ランダムな特徴と化学的記述子を用いてMLモデルの有効性を評価する.
主な方法:
- 原子,電子,振動ディスクリプタを入力機能として使用するMLモデルの適用.
- 遡及的および前向きなテストシナリオが採用されました.
- ランダムに評価された特徴と化学的特徴で訓練されたモデルの性能の比較.
主要な成果:
- 実験設計では,化学的特徴で訓練されたモデルとランダムな特徴で訓練されたモデルを適切に区別することができなかった.
- MLモデルは,検証のための古典的なコントロールに合格しなかった.
- 化学記述子の予測力は決定的に確立されていない.
結論:
- 現在の実験設計は,反応率の予測のためのMLモデルを検証するのに不十分です.
- 化学におけるMLモデルの性能を確実に評価するには,実験設計のさらなる精細化が必要である.
- この研究は,化学研究におけるMLの厳格な検証の重要性を強調しています.
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