De Novo 機械学習化学シフトによる結晶構造の決定
Martins Balodis1, Manuel Cordova1,2, Albert Hofstetter1
1Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.
Journal of the American Chemical Society
|April 13, 2022
まとめ
この研究は,機械学習化学シフトを用いた 固体構造を決定するより速い方法を紹介しています. このアプローチは,医薬品やその他の粉状固体の結晶構造の予測を加速します.
科学分野:
- 固体化学
- 材料科学
- コンピュータ化学
背景:
- 粉状の固体の3D原子構造を決定することは化学において極めて重要です.
- 固体 NMR 化学シフトは構造の決定に価値がありますが,計算的には高価です.
- 現在の方法は高価な結晶構造の予測と DFT 計算に依存しています
研究 の 目的:
- 固体構造の決定のための計算的に効率的な方法を開発する.
- 伝統的な結晶構造予測と DFT 計算の限界を克服する.
- 粉状の固体の原子レベルでの構造分析を より迅速かつ容易に行えるようにする.
主な方法:
- 機械学習で生成された同位体化学シフトを利用した.
- モンテカルロベースの構造決定プロセスを採用した.
- ランダムなガス相形状から構造の決定を開始した.
主要な成果:
- アンピシリン,ピロキシカム,コカインの結晶構造を決定しました.
- 薬分子AZD8329の2つのポリモルフの結晶構造を特定した.
- 伝統的な方法と比較して計算コストを大幅に削減しました.
結論:
- 機械学習による化学シフトは 固体構造の決定を効率的に導くことができます
- 開発された方法は,結晶構造の予測のためのより速い代替案を提供します.
- このアプローチは化学,材料科学,医薬品研究において広く適用可能である.
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