Pharmacophore modeling: advances and pitfalls

Pharmacophore modeling has evolved from a static conceptual framework into a central computational tool in modern drug discovery. Recent advances include multi-pharmacophore strategies that better capture ligand diversity and target flexibility, as well as dynamic pharmacophore models (“dynophores”) derived from molecular dynamics simulations that reflect time-dependent interaction patterns. The integration of artificial intelligence and machine learning has further improved feature extraction, virtual screening accuracy, and predictive performance across discovery pipelines. Despite these advances, pharmacophore modeling remains constrained by conformational bias, limited binding-mode representation, and computational cost. Case studies involving efflux pumps, topoisomerase IIα, and LEDGF/p75–integrase inhibitors illustrate both the strengths and limitations of current methods. Collectively, these developments underscore the value of hybrid approaches to enhance pharmacophore reliability and real-world utility.

• Publication year

  2026

• Venue

  Frontiers in Molecular Biosciences

• Publication date

  2026-01-07

• Fields of study

  Chemistry, Medicine, Computer Science

• Identifiers
  DOI 10.3389/fmolb.2025.1760982PMID 41573741PMCID 12820525
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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