论文标题
探索化学空间的好奇心：深层分子增强学习的内在奖励
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
论文作者
论文摘要
分子的计算机辅助设计有可能破坏药物和材料发现领域。机器学习，尤其是深度学习一直是该领域正在迅速发展的主题。强化学习是一种特别有前途的方法，因为它可以在没有先验知识的情况下进行分子设计。但是，当使用加固学习剂时，搜索空间是广泛而有效的探索。在这项研究中，我们提出了一种算法来帮助有效探索。该算法的灵感来自文献中称为好奇心的概念。我们在三个基准上表明，一个好奇的剂可以找到更好的性能分子。这表明了强化学习代理人一个令人兴奋的新研究方向，可以从自己的动力中探索化学空间。这有可能最终导致意想不到的新分子，而没有人类到目前为止没有想到的新分子。
Computer-aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning, and deep learning, in particular, have been topics where the field has been developing at a rapid pace. Reinforcement learning is a particularly promising approach since it allows for molecular design without prior knowledge. However, the search space is vast and efficient exploration is desirable when using reinforcement learning agents. In this study, we propose an algorithm to aid efficient exploration. The algorithm is inspired by a concept known in the literature as curiosity. We show on three benchmarks that a curious agent finds better performing molecules. This indicates an exciting new research direction for reinforcement learning agents that can explore the chemical space out of their own motivation. This has the potential to eventually lead to unexpected new molecules that no human has thought about so far.
