基础:一个提供化合物-蛋白质结合亲和力预测数据集的网络服务,减少了相似性偏差
Hyojin Son1, Sechan Lee1, Jaeuk Kim1
1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
BMC bioinformatics
|October 31, 2024
概括
在药物标亲和力预测中的数据集偏差得到证实,这是由蛋白质相似性驱动的. 一个新的网络服务,BASE,提供了减少偏差的数据集,以提高预测模型的准确性和可靠性.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 深度学习对药物向亲和力 (DTA) 预测有希望,但受到数据集偏差的影响.
- 之前的研究指出,基于结构的模型存在偏见,但缺乏解决方案,并且关注范围狭窄.
- 数据集偏差对仅使用化合物或蛋白质特征预测结合亲和力的影响仍然不清楚.
研究的目的:
- 用化合物和蛋白质特征来调查DTA预测中的数据集偏差.
- 评估偏差对当前预测模型的影响.
- 为减少偏见开发解决方案,并提供可访问的资源.
主要方法:
- 使用多层感知子模型分析了八个结合亲和数据库.
- 用于复合指纹分析的统一多重近似和投影.
- 通过控制训练和测试集之间的蛋白质相似性,创建了减少偏差的数据集.
- 开发了绑定亲和相似性探索器 (BASE) 网络服务.
主要成果:
- 证实,由于蛋白质相似性偏差,单独使用化合物特征可以预测化合物-蛋白质结合亲和力.
- 蛋白质相似性,而不是化合物结构,被确定为一致的结合亲缘关系的主要驱动因素.
- 偏差减少的数据集显著改变了模型性能,突出显示了蛋白质相似性的影响.
- 特征重要性分析显示,转向与偏差减少的化合物和相互作用特征.
结论:
- BASE 网络服务提供偏差减少的数据集和现有模型预测.
- 这些资源有助于开发更广泛,更强大的DTA预测模型.
- 这些发现提高了药物发现中DTA预测的准确性和可靠性.
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