Existing drug discovery pipelines take 5-10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space which could be on the order of 1060 . Deep generative models can model the underlying probability distribution of both the physical structures and property of drugs and relate them nonlinearly. By exploiting patterns in massive datasets, these models can distill salient features that characterize the molecules. Generative Adversarial Networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity towards binding with the receptor for a target disease. However, classical GANs cannot explore certain regions of the chemical space and suffer from curse-of-dimensionality. A full quantum GAN may require more than 90 qubits even to generate QM9-like small molecules. We propose a qubit-efficient quantum GAN with a hybrid generator (QGAN-HG) to learn richer representation of molecules via searching exponentially large chemical space with few qubits more efficiently than classical GAN. The QGANHG model is composed of a hybrid quantum generator that supports various number of qubits and quantum circuit layers, and, a classical discriminator. QGAN-HG with only 14.93% retained parameters can learn molecular distribution as efficiently as classical counterpart. The QGAN-HG variation with patched circuits considerably accelerates our standard QGANHG training process and avoids potential gradient vanishing issue of deep neural networks. Code is available on GitHub https://github.com/jundeli/quantum-gan.

中文翻译：

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小分子药物发现的量子生成模型

现有的药物发现管道需要5到10年的时间，耗资数十亿美元。计算方法旨在从称为化学空间的整个分子和固态化合物区域进行采样，其大小可能约为1060。深度生成模型可以对药物的物理结构和属性的潜在概率分布进行建模，并将它们非线性关联。通过利用大量数据集中的模式，这些模型可以提取出表征分子的显着特征。生成对抗网络（GANs）通过产生遵循化学和物理特性并显示出与目标疾病受体结合的亲和力的分子结构来发现候选药物。但是，经典GAN无法探索化学空间的某些区域，并且会遭受维度诅咒的困扰。一个全量子GAN甚至可能需要90多个量子位才能生成类似QM9的小分子。我们提出了一种具有混合生成器（QGAN-HG）的量子位有效量子GAN，以通过比经典GAN更有效地搜索具有很少量子位的指数大化学空间来学习更丰富的分子表示。QGANHG模型由支持各种数量的量子位和量子电路层的混合量子生成器以及经典的鉴别器组成。仅保留了14.93％的参数的QGAN-HG可以像经典方法一样高效地学习分子分布。带有贴片电路的QGAN-HG变化大大加快了我们标准的QGANHG训练过程，并避免了深度神经网络潜在的梯度消失问题。可以在GitHub https://github.com/jundeli/quantum-gan上找到代码。