FP-MAP:一个基于指纹的广泛的分子活动预测工具库
1Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway.
Frontiers in chemistry
|August 31, 2023
概括
这项研究介绍了FP-MAP,这是一份用于药物发现的机器学习 (ML) 模型的综合图谱. FP-MAP预测了各种疾病的生物活性,改善了对挑战性条件的成功发现和化合物识别.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 药物发现 药物发现
背景情况:
- 药物发现是一个复杂的,资源密集的过程.
- 机器学习 (ML) 有助于击中发现和领先识别.
- 现有的ML模型往往只关注特定的疾病或活动.
研究的目的:
- 开发FP-MAP,这是一个基于指纹的预测模型的广泛地图.
- 涵盖各种各样的生物活性,包括被忽视的热带疾病和阿尔茨海默病的目标.
- 提高ML在药物发现决策中的效率和范围.
主要方法:
- 评估了大约4000个分类/回归模型.
- 在各种生物活性数据集中利用了12个不同的分子指纹.
- 根据测试组的AUC值,选择了表现最佳的模型.
主要成果:
- 开发了FP-MAP,这是一个具有AUC值从0.62到0.99.9的预测模型图谱.
- 模型涵盖了广泛的疾病,包括病毒,细菌,寄生虫和神经退行性疾病.
- 将最佳模型集成到一个可访问的图形用户界面中.
结论:
- FP-MAP为加速药物发现提供了宝贵的资源.
- 该地图书增强了对各种治疗领域的化合物生物活性的预测.
- 易于使用的界面有助于在识别潜在药物候选物时应用ML.
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