分子相似性用于药物发现,目标预测和化学空间可视化
1Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern. jean-louis.reymond@unibe.ch.
Chimia
|December 9, 2023
概括
量化分子相似性有助于药物发现. 新的计算工具有助于发现强效的选择性药物抑制剂,并预测非目标效应,加速研究.
科学领域:
- 计算化学和化学信息学
- 药理学和药物发现
- 结构生物学和生物物理学
背景情况:
- 分子相似性对于药物发现和优化至关重要.
- 了解离子通道和膜载体是治疗各种疾病的关键.
- 预测药物特性和相互作用的现有方法存在局限性.
研究的目的:
- 审查用于量化药物发现中的分子相似性的计算方法.
- 介绍用于优化候选药物和预测非目标效应的新工具.
主要方法:
- 开发了一个3D分子形状和药比较算法.
- 利用脚手架跳跃来优化弱抑制剂.
- 结合分子相似性搜索与ChEMBL数据库进行非目标预测.
- 创建了交互式化学空间地图.
主要成果:
- 发现了TRPV6,TRPM4,内分泌大麻素运输,DMT1和ZIP8.8的强效和选择性抑制剂.
- 成功预测了候选药物的潜在非目标效应.
- 开发了工具,以帮助选化合物选择和分析.
结论:
- 计算分子相似性工具显著增强药物发现和优化过程.
- 开发的方法有助于识别新型治疗剂,并预测潜在的副作用.
- 在线工具可用于支持研究人员在化学空间探索和药物开发.
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