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<p>Lung cancer remains the leading cause of cancer death globally, highlighting an urgent need for therapies that can overcome acquired resistance. The epidermal growth factor receptor (EGFR) is a well-validated target in non-small-cell lung cancer (NSCLC)however, the continuous emergence of resistance mutations necessitates the exploration of chemically diverse scaffolds with potent binding affinity against the drug-resistant T790M mutant. Here, we present a streamlined, hierarchical in silico discovery framework designed to accelerate the identification of novel EGFR inhibitors targeting the T790M/ L858R resistance mutations. By integrating pharmacophore modeling, 3D-QSAR, and AI-driven toxicity assessments, this study demonstrates a cost-effective lead optimization protocol that significantly reduces the experimental burden associated with early-stage drug discovery. Top matches from three optimal pharmacophoric hypotheses were docked into the EGFR structure (PDB: 3IKA) and ranked by predicted binding affinity. Seven chemically diverse lead compounds were identified with docking scores between − 11.015 and − 10.128&#xa0;kcal·mol⁻<sup>1</sup>. All leads satisfied key drug-like filters (molecular weight &lt; 500&#xa0;Da, fitness score &gt; 0.5, ≤ 7 hydrogen bond donors, ≤ 10 acceptors, and logP between − 2.856 and − 0.084) and showed acceptable in silico ADME/Tox profiles. Molecular dynamics simulations of the top ligand–receptor complexes supported stable binding modes and persistent intermolecular interactions. In silico ADME and toxicity predictions further supported their drug-likeness, indicating good absorption, minimal hepatotoxicity, and low carcinogenic risk. These findings nominate four promising candidates for experimental validation and medicinal chemistry optimization as potential EGFR inhibitors to address T790M-mediated resistance in NSCLC.</p> Graphical Abstract <p></p>

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Computational Identification of Novel EGFR Inhibitors Containing Substituted Pyrimidine Scaffolds Targeting T790M/L858R Mutations

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Shashikant Bhandari,
  • Kshitij Fattepure,
  • Shital Patil,
  • Somdatta Chaudhari,
  • Yash Shimpi,
  • Aditi Saundarkar,
  • Pratik Lohakare,
  • Milind Nikalje,
  • Sneha Pisolkar

摘要

Lung cancer remains the leading cause of cancer death globally, highlighting an urgent need for therapies that can overcome acquired resistance. The epidermal growth factor receptor (EGFR) is a well-validated target in non-small-cell lung cancer (NSCLC)however, the continuous emergence of resistance mutations necessitates the exploration of chemically diverse scaffolds with potent binding affinity against the drug-resistant T790M mutant. Here, we present a streamlined, hierarchical in silico discovery framework designed to accelerate the identification of novel EGFR inhibitors targeting the T790M/ L858R resistance mutations. By integrating pharmacophore modeling, 3D-QSAR, and AI-driven toxicity assessments, this study demonstrates a cost-effective lead optimization protocol that significantly reduces the experimental burden associated with early-stage drug discovery. Top matches from three optimal pharmacophoric hypotheses were docked into the EGFR structure (PDB: 3IKA) and ranked by predicted binding affinity. Seven chemically diverse lead compounds were identified with docking scores between − 11.015 and − 10.128 kcal·mol⁻1. All leads satisfied key drug-like filters (molecular weight < 500 Da, fitness score > 0.5, ≤ 7 hydrogen bond donors, ≤ 10 acceptors, and logP between − 2.856 and − 0.084) and showed acceptable in silico ADME/Tox profiles. Molecular dynamics simulations of the top ligand–receptor complexes supported stable binding modes and persistent intermolecular interactions. In silico ADME and toxicity predictions further supported their drug-likeness, indicating good absorption, minimal hepatotoxicity, and low carcinogenic risk. These findings nominate four promising candidates for experimental validation and medicinal chemistry optimization as potential EGFR inhibitors to address T790M-mediated resistance in NSCLC.

Graphical Abstract