Title: ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?,
Abstract: Generating molecules with desired chemical properties is important for drug
discovery. The use of generative neural networks is promising for this task.
However, from visual inspection, it often appears that generated samples lack
diversity. In this paper, we quantify this internal chemical diversity, and we
raise the following challenge: can a nontrivial AI model reproduce natural
chemical diversity for desired molecules? To illustrate this question, we
consider two generative models: a Reinforcement Learning model and the recently
introduced ORGAN. Both fail at this challenge. We hope this challenge will
stimulate research in this direction.