Re-evaluating sample efficiency in de novo molecule generation

Understand

De novo molecule generation can suffer from data inefficiency; requiring large amounts of training data or many sampled data points to conduct objective optimization.

• The latter is a particular disadvantage when combining deep generative models with computationally expensive molecule scoring functions (a.k.a.
• oracles) commonly used in computer-aided drug design.
• Recent works have therefore focused on methods to improve sample efficiency in the context of de novo molecule drug design, or to benchmark it.