共识集团多目标神经网络化学化合物的焦虑活性模型及其用于多目标药解体设计的使用
Pavel M Vassiliev1,2, Dmitriy V Maltsev2,3, Alexander A Spasov1,2,3
1Laboratory for Information Technology in Pharmacology and Computer Modeling of Drugs, Research Center for Innovative Medicines, Volgograd State Medical University, 39 Novorossiyskaya Street, Volgograd 400087, Russia.
Pharmaceuticals (Basel, Switzerland)
|May 27, 2023
概括
一个新的神经网络模型通过分析17个生物点的复合对接能量来预测焦虑活性. 确定了四个关键标 (ADRA1B,ADRA2A,AGTR1,NMDA-Glut),导致设计了新的多标药,以提高药物发现.
科学领域:
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能
背景情况:
- 焦虑障碍很普遍,需要开发有效的抗焦虑化合物.
- 了解抗焦虑活性背后的分子机制对于合理的药物设计至关重要.
- 含的异环化学类型是开发抗焦虑剂的有希望的类别.
研究的目的:
- 开发一个多目标神经网络模型,根据复合对接能量预测抗焦虑活性.
- 确定化学化合物的抗焦虑作用的关键生物点.
- 设计新型的多目标药,以提高抗焦虑药物的发现.
主要方法:
- 开发了一个分类共识集合多目标神经网络模型.
- 选择了17个与抗焦虑活性相关的生物标.
- 进行了敏感性分析,以确定重要的生物点.
- 使用计算方法构建了单标和多标药.
主要成果:
- 该模型成功预测了三种焦虑缓解活性水平.
- 四个生物点 (ADRA1B,ADRA2A,AGTR1,NMDA-Glut) 被确定为最重要的.
- 八个单向药和两个多向药为特定的化合物类别建造.
- 药体现了与关键生物标的通用相互作用特征.
结论:
- 开发的神经网络模型为预测抗焦虑活性提供了一个强大的平台.
- 确定关键的生物标有助于集中药物设计工作.
- 新型的多目标药为开发具有更高效率的新抗焦虑药物提供了有前途的支架.
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