通过基于联体的生成模型,分子对接和分子动力学模拟来识别潜在的PIM-2抑制剂
Tianli Qin1,2, Yijian Wang1, Miaomiao Kong1
1The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Molecular diversity
|July 2, 2024
概括
研究人员开发了一种人工智能驱动的生成模型,以设计新的Moloney-2 (PIM-2) 激酶抑制剂的Proviral整合. 该模型确定了五种具有高结合能和稳定的相互作用的有前途的候选药物,为PIM-2相关疾病提供了潜在的新疗法.
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:
- 莫洛尼-2 (PIM-2) 激酶的Proviral整合是各种癌症和其他疾病的验证治疗标.
- 目前,没有临床上可用的PIM-2抑制剂,这突出显示了尚未满足的医疗需求.
研究的目的:
- 开发一种高效的人工智能 (AI) 驱动的生成模型,用于新的PIM-2抑制剂设计.
- 发现具有强大治疗潜力的新型PIM-2抑制剂.
主要方法:
- 使用双向长期短期记忆 (BiLSTM) 框架和转移学习构建了一个生成模型.
- 该模型生成了一个新的PIM-2小分子库,通过分子对接和支架相似性进行虚拟选.
- 进行了分子动力学 (MD) 模拟 (100 ns 和 500 ns),以评估顶级打击化合物与 PIM-2 的稳定性和结合相互作用.
主要成果:
- 人工智能生成模型展示了有效的de novo分子生成能力.
- 确定了10个初始打击化合物,其中5个化合物 (C3,C4,C5,C8,C9) 对PIM-2的结合能比积极对照 (3YR) 高.
- MD模拟证实了这五种化合物与PIM-2的活性部位和链区域的稳定相互作用,与阳性对照相比显示较低和更稳定的RMSD值.
结论:
- 该研究成功开发了一种高效的AI生成模型,用于PIM-2抑制剂设计.
- 五种新型化合物被确定为潜在的PIM-2抑制剂,具有有前途的治疗疗效.
- 这项工作为加速PIM-2抑制剂研究和药物发现提供了宝贵的平台.
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