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アリルおよびアルキルブロミドのNi-フォトレドックスクロスエレクトロフィールカップリングの一般化モデルを構築するためのアクティブ・ラーニングの適用
Lucas W Souza1, Nathan D Ricke1, Braden C Chaffin2
1Global Discovery Chemistry, Novartis, Cambridge, Massachusetts 02139, United States.
Journal of the American Chemical Society
|May 22, 2025
まとめ
この研究は,化学的宇宙探査を大幅に改善し,反応率を予測する機械学習モデルのアクティブ・ラーニングアプローチを導入します. この方法は,ランダムなデータ選択を上回る,最小限のデータで広大な基板空間を効率的にマッピングします.
科学分野:
- コンピュータ化学
- 化学における機械学習
- 有機合成
背景:
- 収穫予測のための機械学習モデルの開発は,広大な条件と基板空間を探索する上で課題に直面しています.
- ハイ・スループット・実験 (HTE) はデータ生成に不可欠ですが,化学的多様性を操作するための効率的な戦略が必要です.
研究 の 目的:
- Ni/フォトレドックス触媒によるクロスエレクトロフィールカップリングにおける基板空間をマッピングするためのアクティブ・ラーニング戦略を開発し,検証する.
- 大きな仮想化学空間のためのデータ効率の良い収穫予測モデルを構築する.
主な方法:
- 収益予測モデルを迅速に構築するために,アクティブ・ラーニング,特に不確実性クエリを使用しました.
- ランダムな森林モデルを構築するために,密度関数理論 (DFT) と差異モーガンの指紋を使用しました.
- 最小限の追加データを用いて最初のモデルを新しい化学空間に拡張することに焦点を当てました.
主要な成果:
- 22,240の化合物のモデルが400未満のデータポイントを使って作られました.
- このモデルは100回未満の追加反応で 33,312の化合物に成功しました.
- アクティブ・ラーニング・モデルは ランダムに選択されたデータに基づいたモデルを大幅に上回りました
結論:
- アクティブ・ラーニングは,DFTフィチュライゼーションと組み合わせて,大規模な反応のためのデータ効率の良い予測モデリングを可能にします.
- アルキルラジカルLUMOエネルギーなどのDFTの重要な特徴は,モデルの性能と一般化にとって重要である.
- このアプローチは,合成有機化学のコミュニティが予測モデルを効率的に構築するのに役立ちます.
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