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基于深度学习的药物类化合物数据库的构建及其在HsDHODH抑制剂的虚拟查中的应用

Wei Xia¹, Jin Xiao², Hengwei Bian²

¹Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Methods (San Diego, Calif.)

|March 22, 2024

概括

这项研究使用循环神经网络 (RNN) 和长期短期记忆 (LSTM) 细胞生成了一个新的类似药物化合物数据库. 新的数据库显示了虚拟查和药物发现的前景.

科学领域:

计算化学是一种计算化学.
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:

虚拟选依赖于复合数据库,但商业选择具有专利保护和毒性等局限性.
开发新的,高质量的化合物库对于有效的药物发现至关重要.

研究的目的:

创建一个新的虚拟查化合物数据库,具有可取的类似药物的特性.
克服现有的商业复合数据库的局限性.

主要方法:

使用具有长期短期记忆 (LSTM) 细胞的生成性循环神经网络 (RNN).
从DrugBank数据库中对药物化合物的训练模型.
创建了26316种新型化合物的数据库.

主要成果:

生成的化合物数据库表现出良好的类似药物的特性和新的化学骨干.
使用化学空间,ADME特性,碎片化和可合成性分析进行评估.
通过对接和绑定自由能量计算,确定了具有新骨干的潜在种植物化合物.

结论:

新型化合物数据库适用于虚拟查应用.
该方法成功生成了具有潜在治疗价值的多种化合物.
这种方法为构建类似药物的化合物库提供了一个有希望的替代方案.

关键词:

深度学习 (Deep Learning) 是一种深度学习.药品银行药品银行这就是HsDHODH.经常性的神经网络.短期记忆短期记忆是指短期记忆.虚拟查虚拟查

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