Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.
J Chem Inf Model. 2022 Sep 12;62(17):3970-3981. doi: 10.1021/acs.jcim.2c00602. Epub 2022 Aug 31.
The early stages of the drug design process involve identifying compounds with suitable bioactivities via noisy assays. As databases of possible drugs are often very large, assays can only be performed on a subset of the candidates. Selecting which assays to perform is best done within an active learning process, such as batched Bayesian optimization, and aims to reduce the number of assays that must be performed. We compare how noise affects different batched Bayesian optimization techniques and introduce a retest policy to mitigate the effect of noise. Our experiments show that batched Bayesian optimization remains effective, even when large amounts of noise are present, and that the retest policy enables more active compounds to be identified in the same number of experiments.
药物设计过程的早期阶段涉及通过嘈杂的测定来识别具有合适生物活性的化合物。由于可能药物的数据库通常非常大，因此只能在候选药物的子集上进行测定。在主动学习过程（例如分批贝叶斯优化）中，选择要执行的测定最佳，目的是减少必须执行的测定数量。我们比较了噪声如何影响不同的分批贝叶斯优化技术，并引入了重新测试策略以减轻噪声的影响。我们的实验表明，即使存在大量噪声，分批贝叶斯优化仍然有效，并且重新测试策略可以在相同数量的实验中识别出更多具有活性的化合物。
