Abstract：
The Kirsten rat sarcoma viral G12C (KRASG12C) protein is one of the most common mutations in non-small-cell lung cancer (NSCLC). KRASG12C inhibitors are promising for NSCLC treatment, but their weaker activity in resistant tumors is their drawback. This study aims to identify new KRASG12C inhibitors from among the FDA-approved covalent drugs by taking advantage of artificial intelligence. The machine learning models were constructed using an extreme gradient boosting (XGBoost) algorithm. The models can predict KRASG12C inhibitors well, with an accuracy score of validation = 0.85 and Q2Ext = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predicted to be active inhibitors. Afatinib obtained the highest predictive log-inhibitory concentration at 50% (pIC50) value against KRASG12C protein close to the KRASG12C inhibitors. Only afatinib, neratinib, and zanubrutinib covalently bond at the active site like the KRASG12C inhibitors in the KRASG12C protein (PDB ID: 6OIM). Moreover, afatinib, neratinib, and zanubrutinib exhibited a distance deviation between the KRASG2C protein-ligand complex similar to the KRASG12C inhibitors. Therefore, afatinib, neratinib, and zanubrutinib could be used as drug candidates against the KRASG12C protein. This finding unfolds the benefit of artificial intelligence in drug repurposing against KRASG12C protein.
摘要：
Kirsten大鼠肉瘤病毒G12C（KRASG12C）蛋白是非小细胞肺癌（NSCLC）中最常见的突变之一。KRASG12C抑制剂有望用于NSCLC治疗，但是它们在耐药肿瘤中的活性较弱是它们的缺点。这项研究旨在通过利用人工智能从FDA批准的共价药物中鉴定新的KRASG12C抑制剂。使用极端梯度提升(XGBoost)算法构建机器学习模型。该模型可以很好地预测KRASG12C抑制剂，准确性评分为验证=0.85，Q2Ext=0.76。来自67种FDA共价药物，阿法替尼,达科替尼,阿卡拉布替尼,neratinib,扎努布替尼,dutasteride,预测非那雄胺是活性抑制剂。阿法替尼在接近KRASG12C抑制剂的KRASG12C蛋白的50%(pIC50)值获得最高的预测性对数抑制浓度。只有阿法替尼,neratinib,和扎努鲁替尼在活性位点共价键合，就像KRASG12C蛋白中的KRASG12C抑制剂一样（PDBID：6OIM）。此外，阿法替尼,neratinib,和扎努鲁替尼显示KRASG2C蛋白-配体复合物之间的距离偏差与KRASG12C抑制剂相似.因此,阿法替尼,neratinib,zanubrutinib可用作抗KRASG12C蛋白的候选药物.这一发现揭示了人工智能在针对KRASG12C蛋白的药物再利用中的益处。
