选择性JAK2-JH2全抑制剂的深度学习引导的发现:集成MLP预测建模,基于BREED的库设计和计算验证
Mebarka Ouassaf1, Afaf Zekri1, Shafi Ullah Khan2,3
1Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, Biskra, Algeria.
研究人员开发了一种深度学习模型和碎片杂交策略,以发现新的JAK2抑制剂. BRD1已成为治疗血液和瘤疾病的有希望的选择性全抑制剂候选者.
科学领域:
- 药用化学 医学化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 简氏激酶2 (JAK2) 伪激酶域 (JH2) 是血液学和瘤学疾病的关键治疗标.
- 开发针对JAK2-JH2的选择性全抑制剂对于向治疗至关重要.
研究的目的:
- 使用计算方法识别JAK2-JH2域的新型选择性全抑制剂.
- 验证潜在的抑制剂候选者的疗效和安全性.
主要方法:
- 在1200个针对JAK2的化合物上训练一个多层感知子 (MLP) 深度学习模型.
- 采用基于BREED的片段杂交策略来产生新的分子.
- 使用MLP评分,药理学过器,分子对接和分子动力学 (MD) 和ADMET分析的选化合物.
主要成果:
- 三种化合物 (BRD1,BRD2,BRD3) 被确定为有前途的JAK2-JH2抑制剂.
- 与基准配体相比,BRD1表现出更高的结合亲和力,形状稳定性和对JAK2-JH2残留的选择性.
- MD和ADMET分析表明BRD1.1的稳定性和安全性概况有利.
结论:
- BRD1是选择性全抑制JAK2-JH2.2的强有力的计算候选者.
- 需要进一步的实验验证,以确认BRD1的治疗潜力.
- 所有模型和代码都是公开可用的,以促进未来的研究.
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