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元QSAR和多任务QSAR学习Meta-QSAR and Multi-Task QSAR Learning |
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| 课程网址: | http://videolectures.net/icgeb_soldatova_multi_task_qsar_learning... |
| 主讲教师: | Larisa Soldatova |
| 开课单位: | 伦敦大学金匠学院 |
| 开课时间: | 2019-06-28 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Larisa will present the results of the meta-QSAR project funded by EPSRC (Engineering and Physical Sciences Research Council UK) (‘learning to learn how to design drugs’ EP/K030469/1, EP/K030582/1). Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs. The project team of researchers carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 6 molecular representations, applied to more than 2,700 QSAR problems. They then investigated the utility of algorithm selection for QSAR problems. They found that such a meta-learning approach outperformed the best individual QSAR learning method (i.e. random forests using a molecular fingerprint representation) by up to 13%, on average. It provides evidence for the general effectiveness of meta-learning over base-learning. The meta-QSAR project team also employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. They analysed over a thousands of assay provided by ChEMBL. They carried out feature-based and instance-based MTL to predict drug activities. In addition, they introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. The results of MTL studies were compared with the results of a single task learning, a random forest as the best performing QSAR learner. The results are: instance-based MTL significantly outperformed both, feature-based MTL and the base learner. MTL was significantly improved by incorporating the evolutionary distance between targets. The results of the meta-QSAR project have been made publicly available on OpenML platform. |
| 关 键 词: | 元QSAR; 数据科学; 多任务QSAR学习; meta QSAR项目; QSAR学习的机器学习方法; 开发药物靶点和分析的共性 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-10-14：cyh |
| 最后编审: | 2022-10-14：cyh |
| 阅读次数: | 54 |