ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
This highlights a critical limitation in AI-driven drug discovery, posing a challenge for researchers to improve generative models.
The paper tackles the problem of generative neural networks producing molecules with insufficient diversity for drug discovery, finding that both a Reinforcement Learning model and ORGAN fail to reproduce natural chemical diversity.
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.