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<p>Cancer remains a leading cause of death, with limited effective therapies. The AXL–GAS6 pathway promotes tumor growth, invasion, metastasis, and resistance to apoptosis. Large Language Models (LLMs) can predict drug–target interactions, generate novel molecular scaffolds, and optimize lead compounds. This study aims to design novel small molecules through a computational pipeline integrating commercial LLMs, molecular docking, molecular dynamics (MD), and ADMET evaluation. We combined DeepSeek LLM with conventional computational methods to design AXL inhibitors via three strategies: natural product-based, microbiome-derived, and FDA-approved drug-inspired scaffolds. Structured prompt engineering generated novel candidates, filtered for drug-likeness, synthetic feasibility, and docking score (Glide, Schrödinger). Top hits underwent 100 ns MD simulations and ADMET evaluation (SwissADME, ADMETLab3). AIC1 showed the highest binding affinity (− 10.079&#xa0;kcal/mol), surpassing clinical-stage bemcentinib (− 8.234&#xa0;kcal/mol). MD confirmed stable complexes (RMSD &lt; 3 Å), with AIC1 and AIC4 forming extensive hydrogen bonds. ADMET profiling indicated favorable pharmacokinetics for all, with AIC2 exhibiting the lowest toxicity (hERG inhibition: 34.2%, hematotoxicity: 36.8%) and optimal drug-like properties. This work pioneers LLM-driven in silico design of AXL inhibitors, offering a scalable blueprint for accelerated anticancer drug development.</p>

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Next-generation AI-assisted drug design against cancer: large language models meet conventional in silico methods

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Elina Khanehzar,
  • Fatemeh Shams,
  • Amirsajad Jafari,
  • Alireza Poustforoosh

摘要

Cancer remains a leading cause of death, with limited effective therapies. The AXL–GAS6 pathway promotes tumor growth, invasion, metastasis, and resistance to apoptosis. Large Language Models (LLMs) can predict drug–target interactions, generate novel molecular scaffolds, and optimize lead compounds. This study aims to design novel small molecules through a computational pipeline integrating commercial LLMs, molecular docking, molecular dynamics (MD), and ADMET evaluation. We combined DeepSeek LLM with conventional computational methods to design AXL inhibitors via three strategies: natural product-based, microbiome-derived, and FDA-approved drug-inspired scaffolds. Structured prompt engineering generated novel candidates, filtered for drug-likeness, synthetic feasibility, and docking score (Glide, Schrödinger). Top hits underwent 100 ns MD simulations and ADMET evaluation (SwissADME, ADMETLab3). AIC1 showed the highest binding affinity (− 10.079 kcal/mol), surpassing clinical-stage bemcentinib (− 8.234 kcal/mol). MD confirmed stable complexes (RMSD < 3 Å), with AIC1 and AIC4 forming extensive hydrogen bonds. ADMET profiling indicated favorable pharmacokinetics for all, with AIC2 exhibiting the lowest toxicity (hERG inhibition: 34.2%, hematotoxicity: 36.8%) and optimal drug-like properties. This work pioneers LLM-driven in silico design of AXL inhibitors, offering a scalable blueprint for accelerated anticancer drug development.