论文标题
贝叶斯的最佳信息套件用于早期药物发现
Bayes Optimal Informer Sets for Early-Stage Drug Discovery
论文作者
论文摘要
早期药物发现中的一个重要的实验设计问题是，当目标蛋白知之甚少时，如何优先考虑可用化合物进行测试。当化合物提供了其他潜在相关目标的生物活性数据时，基于告密者的排名（IBR）方法解决了优先级问题。 IBR方法选择一组告示符，然后根据目标在目标上的信息设置进行的新生物活性实验，优先考虑其余化合物。我们将问题形式化为两个阶段的决策问题，并为其解决方案介绍了贝叶斯最佳告密者集（Boise）方法。博伊西（Boise）利用了初始生物活性数据，相关损耗函数和有效的计算方案的灵活模型来解决两步设计问题。我们在两项回顾性研究中评估了博伊西并将其与其他IBR策略进行了比较，其中一项涉及蛋白质激酶抑制，另一个关于抗癌药物敏感性。在两个经验环境中，博伊西都表现出比可用方法更好的预测性能。它在缺少的数据方面也表现得很好，其中使用矩阵完成的方法显示出更差的预测性能。我们在https://github.com/wiscstatman/esdd/boise上提供了Boise的R实施
An important experimental design problem in early-stage drug discovery is how to prioritize available compounds for testing when very little is known about the target protein. Informer based ranking (IBR) methods address the prioritization problem when the compounds have provided bioactivity data on other potentially relevant targets. An IBR method selects an informer set of compounds, and then prioritizes the remaining compounds on the basis of new bioactivity experiments performed with the informer set on the target. We formalize the problem as a two-stage decision problem and introduce the Bayes Optimal Informer SEt (BOISE) method for its solution. BOISE leverages a flexible model of the initial bioactivity data, a relevant loss function, and effective computational schemes to resolve the two-step design problem. We evaluate BOISE and compare it to other IBR strategies in two retrospective studies, one on protein-kinase inhibition and the other on anti-cancer drug sensitivity. In both empirical settings BOISE exhibits better predictive performance than available methods. It also behaves well with missing data, where methods that use matrix completion show worse predictive performance. We provide an R implementation of BOISE at https://github.com/wiscstatman/esdd/BOISE
