在罕见和难以治愈的疾病中用于药物重新定位的新的in silico支架跳跃方法
Mao Tanabe1, Ryuichi Sakate1, Jun Nakabayashi2
1Laboratory of Rare Disease Information and Resource Library, Center for Intractable Diseases and ImmunoGenomics Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Ibaraki, Osaka, Japan.
Scientific reports
|November 8, 2023
概括
这项研究介绍了AI-AAM,这是一种用于罕见疾病中药物重新定位 (DR) 的新型虚拟查方法. 该技术成功地通过保持药理活性并建议改进的替代品来识别潜在的新药候选者.
科学领域:
- 药用化学 医学化学
- 计算机化药物发现技术
- 药理学 药理学是指药理学的学科.
背景情况:
- 对罕见和难治疾病的药物开发带来了重大挑战.
- 药物重新定位 (DR) 提供了一种可行的策略,可以加速为这些疾病发现治疗方法.
研究的目的:
- 开发和评估一种新的虚拟选方法,将氨基酸相互作用映射集成到支架跳转 (AI-AAM) 中,以识别药物重新定位候选者.
- 评估AI-AAM在跳的有效性,同时保持药理活性.
主要方法:
- 开发了AI-AAM方法,将氨基酸相互作用映射与脚手架跳跃集成在一起.
- 该方法最初使用脏相关的氨酸激酶抑制剂作为参考化合物进行了验证.
- AI-AAM应用于五种不同的药物,以确定潜在的药物重新定位候选者.
主要成果:
- 在保持参考化合物的药理活性的同时,成功实现了架跳跃.
- 将AI-AAM应用于五种药物,产生了144种具有不同结构的化合物.
- 在已识别的化合物中,31种针对与其参考相同的蛋白质,而113种针对不同的蛋白质,AI-AAM偏爱功能上类似的化合物.
结论:
- 人工智能-AAM方法有效地识别了具有改善治疗替代品潜力的药物重新定位候选人.
- 已识别的化合物,特别是那些针对不同蛋白质的化合物,被认为也与其参考化合物的标结合.
- 这种新的方法为未来的罕见病药物开发提供了有价值的化合物标信息.
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