人工智能辅助的计算选和对接模拟优先考虑小型分子PCSK9抑制的海洋天然产品
Neelakandan Annamalai Ramalakshmi1, Muthu Kumar Thirunavukkarasu2, Fayaz Shaik3
1Department of Bioscience and Engineering, National Institute of Technology Calicut, Calicut 673601, India.
Current research in translational medicine
|February 12, 2025
概括
研究人员使用人工智能 (AI) 和计算方法发现了新型的蛋白转化酶抑制剂Subtilisin/Kexin类型9 (PCSK9). 这种人工智能驱动的方法成功地确定了潜在治疗用途的有前途的候选药物.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 医学中的人工智能
背景情况:
- SARS-CoV-2 感染与心血管和中枢神经系统并发症有关.
- 蛋白转化酶亚素/素9型 (PCSK9) 在胆固醇调节中发挥作用,并与诸如动脉样硬化等疾病有关,与COVID-19相关.
研究的目的:
- 使用人工智能辅助的虚拟查管道识别人类新型PCSK9抑制剂.
- 探索计算方法在发现COVID-19相关并发症治疗药物的潜力.
主要方法:
- 开发了一种多步骤的in-silico选管道,结合了深度学习 (卷积神经网络),分子对接 (Schrödinger/Glide) 和分子动力学 (MD) 模拟.
- 在现有的中枢神经系统,心血管和抗炎药物上训练了一种深度学习模型,以选CMNPD数据库.
- 利用MD模拟和吉布斯自由能源景观分析来评估结合性亲和关系,并确定入围候选人的关键相互作用.
主要成果:
- 由人工智能驱动的虚拟选管道成功识别了新的PCSK9抑制剂候选者.
- 这些候选药物表现出良好的结合特征,表明潜在的治疗疗效.
- 这项研究表明了人工智能辅助虚拟查在加速药物发现方面的力量.
结论:
- 人工智能辅助的虚拟查是发现新型治疗剂,特别是PCSK9抑制剂的有效策略.
- 鉴定的候选药物需要进一步调查,以治疗与PCSK9相关的疾病.
- 这种计算方法为解决心血管和神经疾病中未满足的医疗需求提供了一个有希望的途径.
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