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<p>Activity cliffs (AC) correspond to large potency differences between highly similar compounds and pose a persistent challenge for both predictive modeling and de novo molecular design, particularly in small and underexplored areas of the chemical space. In this study, we introduce an AC-aware generative framework for the de novo design of anticancer thiazolidinone derivatives relevant to non-small cell lung cancer. We identify difficult regions of structure–activity landscape and extract chemical pattern signals hidden in its discontinuities directly from AC pairs using a dedicated algorithm. These signals are incorporated into a fragment-based generative pipeline to guide molecular construction and candidate selection. Compared with a standard QSAR-guided approach, the proposed framework yields a higher fraction of candidates with favorable docking profiles across multiple cancer-relevant targets and produces ligands with more stable binding modes in molecular dynamics simulations. Among the top five candidates selected by a molecular dynamics, multi-metric consensus combining docking affinity, binding stability (ligand and pocket RMSD/RMSF), and energetic criteria, four were generated using the AC-aware CAFE LATE strategy. The two highest-ranked hits exhibited binding energies of − 7.78 and − 7.70&#xa0;kcal/mol, markedly more favorable than the native reference (− 4.09&#xa0;kcal/mol), and consistently outperformed native ligands across all evaluated stability metrics. Those results suggest that explicit use of AC information can improve the quality of de novo generated small-molecule candidates in data-limited settings.</p>

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De novo design of anticancer 4-thiazolidinone derivatives: a generative framework shaped by activity cliffs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Tomasz Szostek,
  • Maciej Wiśniewski,
  • Davide Ballabio,
  • Viviana Consonni,
  • Dariusz Plewczyński,
  • Daniel Szulczyk

摘要

Activity cliffs (AC) correspond to large potency differences between highly similar compounds and pose a persistent challenge for both predictive modeling and de novo molecular design, particularly in small and underexplored areas of the chemical space. In this study, we introduce an AC-aware generative framework for the de novo design of anticancer thiazolidinone derivatives relevant to non-small cell lung cancer. We identify difficult regions of structure–activity landscape and extract chemical pattern signals hidden in its discontinuities directly from AC pairs using a dedicated algorithm. These signals are incorporated into a fragment-based generative pipeline to guide molecular construction and candidate selection. Compared with a standard QSAR-guided approach, the proposed framework yields a higher fraction of candidates with favorable docking profiles across multiple cancer-relevant targets and produces ligands with more stable binding modes in molecular dynamics simulations. Among the top five candidates selected by a molecular dynamics, multi-metric consensus combining docking affinity, binding stability (ligand and pocket RMSD/RMSF), and energetic criteria, four were generated using the AC-aware CAFE LATE strategy. The two highest-ranked hits exhibited binding energies of − 7.78 and − 7.70 kcal/mol, markedly more favorable than the native reference (− 4.09 kcal/mol), and consistently outperformed native ligands across all evaluated stability metrics. Those results suggest that explicit use of AC information can improve the quality of de novo generated small-molecule candidates in data-limited settings.