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使用深度学习框架的CDK1抑制剂的计算设计,提高了目标亲和力和药物相似性

Zuokun Lu^1,2, Jiayuan Han¹, Yibo Ji¹

¹Food and Pharmacy College, Xuchang University, Xuchang, 461000, Henan, China.

|January 3, 2025

概括

深度学习产生了新的循环素依赖激酶1 (CDK1) 抑制剂,具有增强的结合亲和力和药物相似性. 这些潜在的候选药物显示出癌症治疗的前景,等待进一步的实验验证.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
生物信息学是一种生物信息学.

背景情况:

循环蛋白依赖性激酶1 (CDK1) 对于细胞循环调节至关重要;它的调节失调与癌症有关.
目前的CDK1抑制剂正在临床试验中,但缺乏FDA批准,突出显示了治疗缺口.

研究的目的:

采用深度学习,特别是具有长期短期记忆的循环神经网络 (LSTM),用于生成新型CDK1抑制剂.
用计算方法评估生成的化合物的结合亲和力,分子性质和稳定性.

主要方法:

用长期短期记忆 (LSTM) 进行循环神经网络,用于新药设计.
分子对接以评估与CDK1.1的结合亲和力.
药物相似性的定量估计 (QED) 用于财产评估.
用于稳定性和相互作用分析的分子动力学模拟.

主要成果:

与现有抑制剂相比,生成的配体对CDK1具有更高的结合亲和力 (平均-10.65 kcal/mol).
这些新型化合物与已知的CDK1抑制剂 (平均QED0.547,p <0.001) 相比,具有显著更高的药物相似性 (平均QED0.733).
分子动力学模拟证实了设计的配体与CDK1复合物的稳定性和有利相互作用.

结论:

深度学习有效地产生了新的CDK1抑制剂,提高了目标亲和力和药物相似性.
这些发现提供了有希望的候选人,以解决未得到批准的CDK1向癌症治疗的未满足需求.
在这些计算设计化合物的临床进展之前,广泛的实验验证是至关重要的.

关键词:

这是一种CDK1抑制剂.循环素依赖性激酶1 (CDK1) 是一种模拟MDMD的模拟分子对接是分子对接.经常性的神经网络.

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