Reinforcement learning (RL) over text representations can be effective for
finding high-value policies that can search over graphs. However, RL requires
careful structuring of the search space and algorithm design to be effective in
this challenge. Through extensive experiments, we explore how different design
choices for text grammar and algorithmic choices for training can affect an RL
policy's ability to generate molecules with desired properties. We arrive at a
new RL-based molecular design algorithm (ChemRLformer) and perform a thorough
analysis using 25 molecule design tasks, including computationally complex
protein docking simulations. From this analysis, we discover unique insights in
this problem space and show that ChemRLformer achieves state-of-the-art
performance while being more straightforward than prior work by demystifying
which design choices are actually helpful for text-based molecule design.

通过对不同的文本语法设计和训练算法选择进行广泛的实验，我们提出了一种新的基于强化学习的分子设计算法（ChemRLformer），并通过对 25 个分子设计任务的深入分析，包括计算复杂的蛋白质对接模拟，发现了这个问题领域的独特见解，并展示了 ChemRLformer 在文本分子设计中所取得的最新成果，同时还揭示了哪些设计选择对于文本分子设计实际上有帮助。