The power of machine learning (ML) techniques has captivated the field of small molecule drug discovery. Increasingly, researchers and organisations have employed ML to create more accurate algorithms to improve the efficiency of the discovery process.

To be published, methods have to prove they have improved upon others. Often, methods are tested against the same benchmarks within a field, allowing us to track progress over time. To explore the rate of improvement, I curated the performance on three popular benchmarks. The first benchmark is CASF 2016, used to test the accuracy of methods that predict the binding affinity of experimental determined protein-ligand complexes. Accuracy was measured using the Pearson’s R value between predicted and experimental affinity values.