Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The presented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications.

翻译：深度生成化学模型已成为加速药物发现的强大工具。然而，所有可能的类药分子结构空间极其庞大复杂，这构成了重大障碍，而通过将量子计算机与深度经典网络相结合的混合架构有望克服这些障碍。作为迈向这一目标的第一步，我们构建了一个紧凑型离散变分自编码器（DVAE），其潜在层中采用了缩小尺寸的受限玻尔兹曼机（RBM）。该模型的规模足够小，可适配最先进的D-Wave量子退火器，并允许在ChEMBL数据集中生物活性化合物的子集上进行训练。最终，我们生成了2331种新型化学结构，其药物化学性质与合成可及性均处于ChEMBL分子典型范围。所展示的结果证明了利用现有或即将可用的量子计算设备作为未来药物发现应用测试平台的可行性。