In vitro cellular experimentation with genetic interventions, using for example CRISPR technologies, is an essential step in early-stage drug discovery and target validation that serves to assess initial hypotheses about causal associations between biological mechanisms and disease pathologies. With billions of potential hypotheses to test, the experimental design space for in vitro genetic experiments is extremely vast, and the available experimental capacity - even at the largest research institutions in the world - pales in relation to the size of this biological hypothesis space. Machine learning methods, such as active and reinforcement learning, could aid in optimally exploring the vast biological space by integrating prior knowledge from various information sources as well as extrapolating to yet unexplored areas of the experimental design space based on available data. However, there exist no standardised benchmarks and data sets for this challenging task and little research has been conducted in this area to date. Here, we introduce GeneDisco, a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery. GeneDisco contains a curated set of multiple publicly available experimental data sets as well as open-source implementations of state-of-the-art active learning policies for experimental design and exploration.
翻译:利用CRISPR技术等遗传干预措施的体外细胞实验是早期药物发现和目标验证的一个必要步骤,有助于评估关于生物机制和疾病病理之间因果关系的初步假设。但是,由于有数十亿种潜在的假设需要测试,体外遗传实验的实验设计空间非常巨大,而且即使世界上最大的研究机构,现有实验能力也与这一生物假设空间的大小相比差强人意。 机器学习方法,例如积极和强化学习,可以通过综合各种信息来源的先前知识,以及根据现有数据外推到尚未探索的实验设计空间领域,协助最优化地探索广阔的生物空间。然而,对于这项具有挑战性的任务,目前还没有标准化的基准和数据集,而且在这方面几乎没有开展什么研究。在这里,我们介绍Gene Disco,这是一套评估药物发现实验性设计的积极学习算法的基准套。GeneDisco载有一套可公开获得的多种实验数据集的缩写集,以及用于试验设计和探索的状态积极学习政策的开源实施。