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.

References in corpus (3)

• Towards Principled Methods for Training Generative Adversarial Networks
• The Cramer Distance as a Solution to Biased Wasserstein Gradients
• Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Cited by in corpus (4)

• Deep learning for molecular design - a review of the state of the art
• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions
• A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"