ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?

Understand

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.