将机器学习与分子模拟相结合的工作流揭示了针对BTK和JAK3的潜在双重目标抑制剂
Lu Liu1, Risong Na2, Lianjuan Yang3
1Institute of Theoretical Chemistry, Jilin University, Changchun 130061, China.
Molecules (Basel, Switzerland)
|October 28, 2023
概括
这项研究引入了一种新的计算管道,用于发现针对B细胞淋巴瘤的多目标药物. 该方法确定了一种有前途的双重抑制剂,针对布鲁顿.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 药理学 药理学是指药理学的学科.
背景情况:
- 传统药物开发面临着低成功率和多因素疾病的挑战.
- 对于复杂的疾病,一种药物-一种标方法往往是不够的.
- 多向药物提供了提高疗效和减少不良反应的潜力.
研究的目的:
- 开发和验证用于识别多目标抑制剂的计算管道.
- 发现布鲁顿氨酸激酶 (BTK) 和雅努斯激酶3 (JAK3) 的新型双抑制剂,作为B细胞淋巴瘤的治疗策略.
主要方法:
- 采用了结合机器学习,SHapley添加式扩展 (SHAP) 和分子动力学模拟的管道.
- 自然产品数据库被选为潜在的双重抑制剂.
- 候选抑制剂被评估为药物吸收,分布,新陈代谢,分泌和毒性 (ADMET) 特性.
主要成果:
- 管道成功确定了BTK和JAK3.3的潜在双重抑制剂.
- 选了三种具有可接受ADMET特性的候选抑制剂.
- 化合物CNP0266747被选为最佳选择,表现出有利的结合自由能量和针对BTK和JAK3.3的特定构造.
结论:
- 开发的计算管道对于发现多目标抑制剂是有效的.
- CNP0266747显示出作为B细胞淋巴瘤治疗的双重抑制剂的显著潜力.
- 确定了促进抑制BTK和JAK3的关键残留物和分子特征.
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