Comparison of Traditional and Machine Learning Programs in the Evaluation of Protein-Ligand Binding

Molecular docking, an in-silico method with widespread pharmacological applications, is used to predict the optimal conformation of a protein-ligand complex. Traditionally, it uses search-score algorithms that generate protein-ligand poses and calculate each pose’s binding strength. More recently, artificial intelligence (AI) programs have been developed and trained with protein-ligand datasets. To compare the accuracy of these approaches in site-specific docking, a traditional program and a deep-learning (DL) program were tasked with docking a set of protein-ligand pairs. Upon comparison of the two programs’ results, it was determined that they predict optimal binding conformations with similar accuracy.