Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

De novo drug design with desired biological activitiesis crucialfor developing novel therapeutics for patients. The drug developmentprocess is time- and resource-consuming, and it has a low probabilityof success. Recent advances in machine learning and deep learningtechnology have reduced the time and cost of the discovery processand therefore, improved pharmaceutical research and development. Inthis paper, we explore the combination of two rapidly developing fieldswith lead candidate discovery in the drug development process. First,artificial intelligence has already been demonstrated to successfullyaccelerate conventional drug design approaches. Second, quantum computinghas demonstrated promising potential in different applications, suchas quantum chemistry, combinatorial optimizations, and machine learning.This article explores hybrid quantum-classical generative adversarialnetworks (GAN) for small molecule discovery. We substituted each elementof GAN with a variational quantum circuit (VQC) and demonstrated thequantum advantages in the small drug discovery. Utilizing a VQC inthe noise generator of a GAN to generate small molecules achievesbetter physicochemical properties and performance in the goal-directedbenchmark than the classical counterpart. Moreover, we demonstratethe potential of a VQC with only tens of learnable parameters in thegenerator of GAN to generate small molecules. We also demonstratethe quantum advantage of a VQC in the discriminator of GAN. In thishybrid model, the number of learnable parameters is significantlyless than the classical ones, and it can still generate valid molecules.The hybrid model with only tens of training parameters in the quantumdiscriminator outperforms the MLP-based one in terms of both generatedmolecule properties and the achieved KL divergence. However, the hybridquantum-classical GANs still face challenges in generating uniqueand valid molecules compared to their classical counterparts.