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深いニューラルネットワークとシンボリックAIによる化学合成の計画
Marwin H S Segler1,2, Mike Preuss3, Mark P Waller4
1Institute of Organic Chemistry and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität, Münster, Germany.
Nature
|March 30, 2018
まとめ
この研究では,有機分子合成の計画における速度と精度を大幅に改善する AI 駆動のレトロシンセシスツールが紹介されています. この新しいアプローチは 専門の化学者に匹敵する 高品質の合成経路を生み出します
科学分野:
- 有機化学
- 人工知能
- コンピュータ化学
背景:
- 逆合成は有機分子合成の計画に不可欠です.
- 現在のコンピュータ・アイド・レトロシンセシス・ツールは遅いもので,最適な結果が得られない.
研究 の 目的:
- より速く,より正確なコンピュータ支援レトロシンセシスシステムを開発する.
- 人工知能とモンテカルロツリー検索を活用して 合成経路を発見する
主な方法:
- 統合されたモンテカルロツリー検索と深層ニューラルネットワーク (拡張ポリシーとフィルターネットワーク)
- 公開された有機反応の包括的なデータセットで 訓練されたニューラルネットワーク
- レトロ合成経路の発見に 象徴的なAIを使いました
主要な成果:
- AIシステムは従来の方法と比較して ほぼ2倍の分子を処理します
- この新しいシステムは,既存のルールに基づくコンピュータ・アイド・レトロシンセシスより30倍高速です.
- ダブルブラインドテストでは,AIによって生成された経路は,文献経路と同等であることが示されました.
結論:
- AI主導のレトロシンセシスは 伝統的な計算方法よりも 重要な進歩をもたらします
- 開発されたシステムは,合成計画のための強力で効率的なツールを化学者に提供します.
- このアプローチは複雑な有機分子合成の発見と実現可能性を高めます
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