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<p>Fragment based drug design is like a chess game in that a good or a bad move can dramatically influence the outcome. In the design process, it is important to identify the key binding site residues (hotspots) that can have a substantial impact on ligand potency and efficiency. Here, we introduce FMOPhore algorithm represented with a scoring function named FP-score, which combines Quantum Mechanics Fragment Molecular Orbital calculations with 3D-protein-ligand pharmacophore models. FP-score accurately classifies binding site residues in two classes: 1) Hotspot residues (Delineated into three categories; Anchor, Transient, and Accessible) and 2) non-hotspot residues. We apply our algorithm in two different scenarios: holo-complex and apo-structure, testing its robustness on 46 different protein targets including an experimental case study on drug-resistance hotspots across 829 protein-ligand complexes. We handle protein binding site flexibility using Dy-FMOPhore which improves the detection of hotspots. FMOPhore provides valuable insight for efficient, selective fragment growing and lead optimization strategies.</p>

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FMOPhore for hotspot identification and efficient fragment-to-lead growth strategies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Peter E. G. F. Ibrahim,
  • Simone Altmann,
  • Ulrich Zachariae,
  • David Horn,
  • Ian H. Gilbert,
  • Michael J. Bodkin

摘要

Fragment based drug design is like a chess game in that a good or a bad move can dramatically influence the outcome. In the design process, it is important to identify the key binding site residues (hotspots) that can have a substantial impact on ligand potency and efficiency. Here, we introduce FMOPhore algorithm represented with a scoring function named FP-score, which combines Quantum Mechanics Fragment Molecular Orbital calculations with 3D-protein-ligand pharmacophore models. FP-score accurately classifies binding site residues in two classes: 1) Hotspot residues (Delineated into three categories; Anchor, Transient, and Accessible) and 2) non-hotspot residues. We apply our algorithm in two different scenarios: holo-complex and apo-structure, testing its robustness on 46 different protein targets including an experimental case study on drug-resistance hotspots across 829 protein-ligand complexes. We handle protein binding site flexibility using Dy-FMOPhore which improves the detection of hotspots. FMOPhore provides valuable insight for efficient, selective fragment growing and lead optimization strategies.