通过深度学习 in-silico 药物设计方法,重新审视甲基和光甲基15 基于图书馆的衍生品
Farhan Siddique1,2, Ahmar Anwaar3, Maryam Bashir2,4
1School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.
Frontiers in chemistry
|April 5, 2024
概括
计算方法通过选甲醇酸衍生物与二叶酸减少酶标对抗,确定了新型抗癌药物候选者. 有前途的化合物显示出良好的口服生物可用性和低毒性,需要进一步实验验证用于癌症治疗.
科学领域:
- 计算化学和药物发现
- 药品化学和药理学 药品化学和药理学
背景情况:
- 癌症仍然是全球主要的死亡原因,需要开发新的治疗药物.
- 计算技术为加速发现新型抗癌药物提供了一个有希望的途径.
研究的目的:
- 进行基于QSAR的甲基和光三衍生物的虚拟查,以识别二叶酸减少酶 (DHFR) 的新型抑制剂.
- 使用计算模型预测已识别的化合物的抗癌潜力,ADMET特性和毒性.
主要方法:
- 使用基于深度学习的ADMET参数和多重线性回归 (MPL) 的定量结构-活动关系 (QSAR) 建模.
- 针对DHFR目标对271种甲基甲酸 (MTX) 和光甲酸 (PTX) 衍生物进行虚拟查.
- 通过消息传递神经网络 (MPNN) 和密度函数理论 (DFT) 计算评估ADMET属性;用于验证的分子动力学模拟.
主要成果:
- QSAR模型实现了高预测准确度 (LOO-CV Q2=0.77,R2=0.81) 的结果.
- 虚拟查发现了八种最受影响的化合物 (09, 27, 41, 68, 74, 85, 99, 180) 具有显著的预测抑制活性 (pIC505.85-7.20).
- 选择的化合物表现出良好的口服药物潜力 (Log P 0.19-2.69,生物利用率76.30%-78.46%) 和低临床毒性,其中化合物180显示毒性最小 (8.30%).
结论:
- 鉴定到的化合物显示出与标准药物MTX和PTX相比,具有优越或可比的抗癌潜力.
- 这些化合物代表了新型抗癌疗法的有希望的候选者,等待实验验证.
- 建议进行进一步的体外和体内研究,以确认已识别的热点的抑制潜力和疗效.
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