基于结构的溶液载体载体输送器药物设计中的计算化学:最近的进展和未来的前景
Gao Tu1, Tingting Fu2, Guoxun Zheng3
1Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
计算化学和人工智能加速了溶液载体运输体 (SLC) 的药物发现,这是癌症和糖尿病等疾病的关键目标类. 这些方法有助于理解SLC的结构和功能,用于开发新药.
科学领域:
- 生物化学和结构生物学.
- 计算化学和药物设计.
- 药理学和治疗目标的识别.
背景情况:
- 溶解物载体运输体 (SLC) 是重要的跨膜蛋白调节细胞运输.
- 在450个人类SLC中,超过四分之一是癌症,抑郁症和糖尿病等疾病的治疗点.
- 3D结构 (44个传送器) 的有限可用性阻碍了SLC超级家族的药物开发.
研究的目的:
- 为基于SLC的结构的药物设计提供计算技术的全面概述.
- 为突出应用计算化学和人工智能的近期进展和挑战,以SLC药物发现.
- 为了指导新型SLC调制器的合理设计.
主要方法:
- 使用基于物理的分子建模.
- 采用机器学习和基于深度学习的人工智能 (AI).
- 专注于基于结构的SLC药物设计方法.
主要成果:
- 在溶解物运输过程中表征蛋白质构造.
- 识别可用药的位点,包括隐秘的全位点,用于基质和药物结合.
- 发现特定SLC结合部位的小分子和蛋白质结合剂.
结论:
- 计算化学和人工智能为SLCs的经典药物发现提供了强大的互补方法.
- 计算方法的进步有助于理解SLC结构-功能关系.
- 这些技术对于合理设计针对SLCs的新型治疗剂至关重要.
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