使用机器学习预测模型进行生物活性预测和虚拟查
Noor Fatima Siddiqui1, Pinky Vishwakarma1, Shikha Thakur1
1Department of Pharmacy, Pharmaceutical Chemistry Research Laboratory, Birla Institute of Technology and Science Pilani, Pilani, RJ, India.
Journal of biomolecular structure & dynamics
|January 13, 2024
概括
一个新的机器学习模型预测了酶抑制剂,解决了药物发现中的化学偏差. 随机森林模型被证明是最准确的,识别了潜在的DPP-4抑制剂.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 机器学习是机器学习.
背景情况:
- 对酶抑制剂的预测建模至关重要,但受到化学偏差和缺乏可重复性的限制.
- 现有的模型往往无法解释化学多样性,阻碍了药物发现工作.
研究的目的:
- 开发一种新的机器学习模型,用于预测化学偏差的酶抑制剂.
- 为了评估模型的有效性,使用Dipeptidyl peptidase 4 (DPP-4) 抑制剂,并验证其性能.
主要方法:
- 使用随机森林算法开发机器学习模型.
- 使用各种训练/测试数据与随机分割对模型性能进行比较.
- 在药物银行数据库中对DPP-4抑制剂进行in-silico选.
- 通过分子对接和分子动力学模拟进行验证.
主要成果:
- 随机森林算法在经过测试的机器学习算法中显示了最高的准确性.
- 基于Murcko支架的开发模型有效地解决了化学偏差的担忧.
- 在体查中,从药物银行数据库中确定了两种已知的DPP-4抑制剂.
- 分子对接和动力学模拟证实了该模型的预测可信度.
结论:
- 结合Murcko支架的机器学习模型可以克服酶抑制剂预测中的化学偏差.
- 开发的模型显示了对高效的药物发现和潜在的临床转化有希望.
- 该方法提供了一种可靠的方法来识别新药候选药物.
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