機械学習アプローチを用いたシグマ受容体リガンドの活性と選択性プロファイルの予測
Lisa Lombardo1, Verena Battisti2, Thierry Langer2
1Department of Chemical, Biological, Pharmaceutical and Environmental Sciences (CHIBIOFARAM), University of Messina, Viale Ferdinando D'Alcontres 13, I-98166 Messina, Italy.
Journal of chemical information and modeling
|September 1, 2025
まとめ
機械学習モデルはシグマ受容体のリガンド活性と選択性を予測します. 選択的S1RとS2Rリガンドを特定するために,追加の樹とMordred記述子を用いた多種分類アプローチが最も効果的であることが示された.
科学分野:
- 薬理学と化学情報学
- コンピュータによる薬物発見
背景:
- S1R (CNS,神経保護) とS2R (がん,神経細胞) を含むシグマ受容体 (SR) は,重要な治療標的である.
- 構造的な違いにもかかわらず,SRサブタイプは結合部位の特徴を共有し,標的治療のための選択的リガンドの識別を必要とします.
研究 の 目的:
- シグマ受容体リガンドの活性と選択性を予測するための機械学習モデルを開発し,評価する.
- S1RとS2Rリガンドを区別するための最も効果的な計算ワークフローを特定する.
主な方法:
- シグマ受容体リガンドの高品質のデータセットを整理しました.
- 機械学習の3つのアプローチを採用しました. 分類,回帰,多種分類です.
- 分子記述子 (Mordred,RDKit) と指紋 (ECFP4,ECFP6,MACCS) を,エクストラ・ツリー,ランダム・フォレスト,SVM,k-NN,XGBoostなどのアルゴリズムで利用した.
- ネストおよび5倍クロスバリデーションを用いてモデルを検証し,その後外部バリデーションを行います.
主要な成果:
- エクストラ・ツリーとモルドレッド・ディスクリプタを統合した1段階のマルチ分類アプローチは,優れた予測性能を示した.
- このワークフローは,外部検証で最高の結果を達成し,強力な予測力を示しました.
結論:
- 機械学習,特にマルチ分類は,SRリガンドプロフィールを予測するための強力なツールを提供します.
- 開発されたExtra Trees-Mordredワークフローは,セレクティブS1RとS2Rリガンドを特定するための信頼できる方法を提供し,治療設計を支援します.
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