Automating molecular design using deep reinforcement learning (RL) holds the
promise of accelerating the discovery of new chemical compounds. Existing
approaches work with molecular graphs and thus ignore the location of atoms in
space, which restricts them to 1) generating single organic molecules and 2)
heuristic reward functions. To address this, we present a novel RL formulation
for molecular design in Cartesian coordinates, thereby extending the class of
molecules that can be built. Our reward function is directly based on
fundamental physical properties such as the energy, which we approximate via
fast quantum-chemical methods. To enable progress towards de-novo molecular
design, we introduce MolGym, an RL environment comprising several challenging
molecular design tasks along with baselines. In our experiments, we show that
our agent can efficiently learn to solve these tasks from scratch by working in
a translation and rotation invariant state-action space.

本文提出了一种新颖的基于强化学习的分子设计方式，使用笛卡尔坐标系来扩展分子结构类型，并利用快速量子化学方法对基本物理性质（如能量）进行直接奖励函数的构建。同时，引入了名为 MolGym 的强化学习环境，在该环境中针对多种具有挑战性的分子设计任务成功展开实验，并在此基础上，构建出一种翻译及旋转不变的状态空间，实现了从零开始的高效学习。