提取主要蛋白质标作为可能的候选药物:机器学习评估评估
Subhagata Chattopadhyay1, Nhat Phuong Do2, Darren R Flower3
1Dept. of Computer Science and Engineering, GITAM School of Technology, Gandhi Institute of Technology And Management (GITAM) deemed to be University, Bengaluru, Karnataka, 561203, India.
本研究使用分子对接和机器学习确定MRSA药物开发的主要蛋白质标 (PPT). 它预测有5种高保真性PPT,其中PPT2是下一代抗生素的最佳候选者.
科学领域:
- 计算化学计算化学
- 药物发现 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:
- 识别强效药物标对于开发新的MRSA疗法至关重要.
- 分子对接和机器学习是药物发现的关键工具.
研究的目的:
- 为了确定MRSA候选药物的主要蛋白质标 (PPTs).
- 开发一种高效的算法来提取高亲缘关系联结体 (HALs) 和PPT.
主要方法:
- 来自10个分子对接评估的综合数据.
- 利用机器学习进行数据挖掘和建模 (DDM) 和反向建模 (RM).
- 采用K-平均集群 (KMC) 并根据高斯混合模型 (GMM) 和DBSCAN进行验证.
主要成果:
- 确定了5种高保真性PPT (17%) 与19%的配体结合.
- PPT2 (41.1%的平均HPC),PPT14 (25.46%) 和PPT15 (23.12%) 是最好的候选人.
- 对于集群,KMC和GMM被证明是有效的,而DBSCAN则在噪音方面扎.
结论:
- 开发的算法可以有效地识别MRSA候选药物.
- 这种方法适用于各种病原体,并且在稀疏数据下有效.
- 预测有希望的下一代MRSA药物候选人.
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