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110億以上の化合物の仮想ライブラリにおけるシントンベースのリガンドの発見

Arman A Sadybekov^1,2, Anastasiia V Sadybekov^1,2, Yongfeng Liu^3,4

¹Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.

|December 16, 2021

まとめ

V-SYNTHESは,構造ベースの仮想スクリーニングのための新しい,階層的なアプローチを提供します. この方法では強力な薬剤候補を大量の化学図書館から迅速に特定し計算コストを大幅に削減しヒット率を向上させます

科学分野:

薬剤発見と薬剤化学
コンピュータ化学
化学合成

背景:

構造ベースの仮想リガンドスクリーニングは,早期の薬剤発見に不可欠です.
超大型仮想コンパウンドライブラリには,高度なスクリーニング方法が必要です.
化学図書館の急速な成長に追いつくには既存の方法が困難です

研究の目的:

階層的なスクリーニングのためのモジュラーなシンソンベースのアプローチであるV-SYNTHESを導入します.
ギガスケール化学図書館の効率的なスクリーニングを可能にします.
新薬の発見を加速させるため

主な方法:

V-SYNTHESは階層的・組合せ的戦略を採用しています
スタートポイントとして最適のエスカフォード-シンソンの組み合わせを特定します.
種子の繰り返し処理は,ドッキングスコアが高い完全な分子を選択します.

主要な成果:

V-SYNTHESは,鉛の識別で0.1%未満の化合物をスクリーニングしました.
新種のカンナビノイド抗生物質の試験は,サブミクロモラーリンガンドを含む33%のヒット率を示した.
最適化された類型は高効力 (K_i=0. 9 nM) とCB2 / CB1受容体に対する選択性を示した.

結論:

V-SYNTHESは標準の仮想スクリーニングを効率とヒット率で大幅に上回ります
このアプローチはスケーラブルで,様々なドッキングアルゴリズムに適応し,キナーゼ標的で検証されています.
この方法は,大型コンビネトリアルライブラリのリード発見を加速します.

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