  De novo drug design with desired biological activities is crucial for
developing novel therapeutics for patients. The drug development process is
time and resource-consuming, and it has a low probability of success. Recent
advances in machine learning and deep learning technology have reduced the time
and cost of the discovery process and therefore, improved pharmaceutical
research and development. In this paper, we explore the combination of two
rapidly-developing fields with lead candidate discovery in the drug development
process. First, Artificial intelligence has already been demonstrated to
successfully accelerate conventional drug design approaches. Second, quantum
computing has demonstrated promising potential in different applications, such
as quantum chemistry, combinatorial optimizations, and machine learning. This
manuscript explores hybrid quantum-classical generative adversarial networks
(GAN) for small molecule discovery. We substituted each element of GAN with a
variational quantum circuit (VQC) and demonstrated the quantum advantages in
the small drug discovery. Utilizing a VQC in the noise generator of a GAN to
generate small molecules achieves better physicochemical properties and
performance in the goal-directed benchmark than the classical counterpart.
Moreover, we demonstrate the potential of a VQC with only tens of learnable
parameters in the generator of GAN to generate small molecules. We also
demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this
hybrid model, the number of learnable parameters is significantly less than the
classical ones, and it can still generate valid molecules. The hybrid model
with only tens of training parameters in the quantum discriminator outperforms
the MLP-based one in terms of both generated molecule properties and the
achieved KL divergence.
