使用机器学习方法,为不同类型的循环林依赖性激酶抑制剂的不同亚家族推导一般结构-活性/选择性关系模式
Sara Kaveh1, Ahmad Mani-Varnosfaderani2, Marzieh Sadat Neiband3
1Chemometrics and Cheminformatics Laboratory, Department of Analytical Chemistry, Tarbiat Modares University, Tehran, Iran.
Scientific reports
|July 3, 2024
概括
机器学习模型预测了循环林依赖激酶 (CDK) 抑制剂的活性和选择性. 这项研究确定了开发具有较少副作用的向癌症治疗的关键分子特征.
科学领域:
- 药用化学 医学化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 循环素依赖激酶 (CDK) 对于细胞循环调节至关重要,是癌症治疗的关键标.
- 开发选择性CDK抑制剂具有挑战性,但对于最大限度地减少非目标副作用至关重要.
研究的目的:
- 为了推导出用于建模CDK抑制剂选择性和活性的一般结构-活性关系 (SAR) 模式.
- 利用机器学习来预测和识别强效和选择性CDK抑制剂.
主要方法:
- 从绑定DB收集了8592个具有与CDK1,CDK2,CDK4,CDK5和CDK9的结合亲和性的小分子.
- 计算分子描述器和训练有素的监督Kohonen网络 (SKN) 和反传播人工神经网络 (CPANN) 模型.
- 使用十倍交叉验证和外部测试集验证模型;对200万个PubChem分子进行虚拟查.
主要成果:
- 在外部测试集上,SKN模型实现了0.75到0.94的预测准确度.
- 确定分子描述符,如水友性和极地总表面积,用于活动和选择性映射.
- 虚拟选为SKN模型提供了从0.72到1.00的接收器运行特征曲线下的面积.
结论:
- 这项研究提供了一个强大的机器学习框架,用于预测CDK抑制剂的特性.
- 开发了活性和选择性CDK抑制剂的模型并确定了化学空间区域.
- 有助于解决CDK选择性的挑战,帮助开发更安全的癌症治疗方法.
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