Machine learning (ML) and deep learning (DL) approaches can mitigate these limitations. ML can help determine features of the receptor-ligand complex that correlate with affinity to augment scoring functions and improve docking and screening power. For example, the 𝚫vinaRF20 scoring function combines twenty ligand, protein and pharmacophore features selected among a larger set of candidate features with random forest regression . Post-scoring with 𝚫vinaRF20 improved docking and screening (ranking) power compared to the baseline AutoDock Vina scoring function . A major advantage of learning approaches compared to conventional methods is that they can capitalize on the rapidly increasing availability of data to improve accuracy .