Recent developments in artificial intelligence and automation support a new drug design paradigm: autonomous drug design. Under this paradigm, generative models can provide suggestions on thousands of molecules with specific properties, and automated laboratories can potentially make, test and analyze molecules with minimal human supervision. However, since still only a limited number of molecules can be synthesized and tested, an obvious challenge is how to efficiently select among provided suggestions in a closed-loop system. We formulate this task as a stochastic multi-armed bandit problem with multiple plays, volatile arms and similarity information. To solve this task, we adapt previous work on multi-armed bandits to this setting, and compare our solution with random sampling, greedy selection and decaying-epsilon-greedy selection strategies. According to our simulation results, our approach has the potential to perform better exploration and exploitation of the chemical space for autonomous drug design.
翻译:人工智能和自动化的最新发展支持一种新的药物设计范式:自主药物设计。在这个范式下,基因模型可以为具有特定特性的数千个分子提供建议,而自动化实验室有可能在最低限度的人力监督下制造、测试和分析分子。然而,由于仍然只有数量有限的分子可以合成和测试,一个明显的挑战是如何在闭环系统中有效地在提供的建议中选择。我们把这个任务设计成一个具有多种戏剧、挥发性手臂和类似信息的随机多臂强盗问题。为了解决这个问题,我们调整了以前关于多臂强盗的工作,将我们的解决方案与随机抽样、贪婪选择和腐蚀-精英-基因选择战略进行比较。根据我们的模拟结果,我们的方法有可能更好地探索和利用化学空间进行自主药物设计。