DeepGAN: Generating Molecule for Drug Discovery Based on Generative Adversarial Network

Abstract

As one of the most core links in the pharmaceutical industry, drug discovery is an important direction for the application of artificial intelligence technology. It is still a huge challenge to accelerate the discovery process. To address it, we have developed a generative model for de novo small-molecule based on Generative Adversarial Network algorithm called DeepGAN. It is worth mentioning that we make DeepSMILES as training object, which has avoided the limitations of SMILES. And the addition of reinforcement learning keeps away from non-differentiable problem of the discriminator. The model is trained to optimize the rewards and adversarial loss in specific areas through strategy gradient. In this way, DeepGAN compares favorably to ORGAN and its derivatives OR(W)GAN and Naive RL which have been already well-tested. The experiments indicate our model can create molecules which can maintain molecular diversity, increase validity and show improvement in the desired metrics.