顺序对比和深度学习模型用于识别选择性丁胆酶抑制剂
Mustafa Kemal Ozalp1, Patricia A Vignaux1, Ana C Puhl1
1Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
Journal of chemical information and modeling
|March 27, 2024
概括
研究人员开发了机器学习模型,以寻找针对阿尔茨海默病 (AD) 的选择性丁胆酶 (BChE) 抑制剂. 这种方法成功地以高精度识别了有前途的候选药物,为AD症状治疗提供了新的策略.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 神经科学是一个神经科学.
背景情况:
- 晚期阿尔茨海默氏症 (AD) 对症状治疗提出了挑战.
- 在AD中使用的乙胆化酶 (AChE) 抑制剂可能会引起严重的副作用.
- 乙基胆酶 (BChE) 是阿尔茨海默病的潜在治疗点,选择性抑制剂 (BIs) 是一个有前途的替代品.
研究的目的:
- 使用各种机器学习 (ML) 策略来识别选择性BCHE抑制剂 (BI).
- 在预测BCHE选择性方面,优化ML模型的精度.
- 为了比较监督对比学习 (CL),深度学习 (DL) 和随机森林 (RF) 识别BI的有效性.
主要方法:
- 雇员监督对比学习 (CL),深度学习 (DL) 和随机森林 (RF) ML模型.
- 在单一和顺序建模方法中配置模型.
- 通过使用开发的ML模型,虚拟选了500万种化合物的库.
- 在实验中验证了20种最可预测的化合物.
主要成果:
- 从虚拟屏幕上识别选择性BChE抑制剂的成功率达到了35%.
- 在20种测试的化合物中,有7种对BChE比ACHE具有选择性.
- 在预测选择性BI方面,ML模型被证明是非常有效的.
结论:
- 机器学习,特别是监督的CL,DL和RF,为识别选择性BCHE抑制剂提供了高效的策略.
- 这种"in silico"的方法可以加速对阿尔茨海默病的新型治疗剂的发现.
- 已识别的选择性BI要求对AD症状治疗进行进一步调查.
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