用于药物发现的大规模多任务网络
Massively Multitask Networks for Drug Discovery
摘要：
大规模多任务神经架构为药物发现提供了一个学习框架，可以综合来自许多不同生物来源的信息。为了大规模训练这些架构，我们从公共来源收集了大量数据，创建了一个包含200多个生物靶标近4000万个测量值的数据集。我们通过一系列的实证研究来调查多任务框架的几个方面，并获得了一些有趣的结果：（1）大规模多任务网络获得的预测准确性显著优于单任务方法，（2）多任务网络的预测能力随着附加任务和数据的添加而提高，（3）数据总量和任务总数都对多任务改进有显著贡献;（4）多任务网络对训练集中以外的任务的可移植性有限。我们的研究结果强调了需要更大的数据共享和进一步的算法创新来加速药物发现过程。
英文摘要：
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results: (1) massively multitask networks obtain predictive accuracies significantly better than single-task methods, (2) the predictive power of multitask networks improves as additional tasks and data are added, (3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and (4) multitask networks afford limited transferability to tasks not in the training set. Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
