通过从生物活性反中积极学习,提高虚拟查的成功率
Xun Deng1,2, Junlong Liu2, Zhike Liu3
1School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China.
Journal of chemical theory and computation
|April 16, 2025
概括
这项研究引入了从生物活性反 (ALBF) 中积极学习的框架,以改善药物发现虚拟查. 通过代使用湿实验室生物活性数据来改进分子排名,提高准确性和成本效益,ALBF提高了命中率.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 药物发现中的虚拟选方法由于简化得分功能,通常会产生较低的命中率.
- 实验室验证的高成本限制了对潜在候选药物的探索.
- 目前的虚拟查忽视了来自湿实验室实验的宝贵生物活性反.
研究的目的:
- 引入生物活性反 (ALBF) 框架的积极学习,以提高虚拟查的成功率.
- 提高识别潜在候选药物的准确性和成本效益.
- 为了提高查结果,利用针对特定目标的生物活性见解.
主要方法:
- 开发了一种新的查询策略,考虑了评估质量和对其他分子的影响.
- 实施了一种高效的得分优化策略,以传播生物活性反到类似的分子.
- 使用代湿实验室实验预算对DUD-E和LIT-PCBA基准进行了ALBF框架的评估.
主要成果:
- 在ALBF协议中,在DUD-E上平均提高了60%的最高100个命中率,在LIT-PCBA上平均提高了30%.
- 这些改善在10轮实验中观察到50到200个生物活性查询.
- 该框架在各种基准值子集中表现一致.
结论:
- 通过生物活性反 (ALBF) 的积极学习框架显著提高了虚拟查的准确性.
- 在药物发现管道中,ALBF提高了实验室测试的成本效益.
- 这种方法在优化生物活性分子的识别方面具有巨大的潜力.
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