Understanding the bound, three-dimensional (3D) conformation of small molecules to their targets is critical for drug discovery. Molecular docking serves as an in silico tool for the prediction of protein-ligand binding conformations. Molecular docking is generally composed of two steps: sampling, in which the conformational space of the complex is explored, and scoring, where a score denoting the quality of the protein-ligand complex is given to rank the conformations. Traditional scoring functions include force field, empirical, and knowledgebased approaches. Force field-based scoring functions employ molecular mechanics-based energy functions to analyze atomic interactions and calculate scores. Empirical scoring functions assign weights to the energetic interaction terms to better fit experimentally determined binding affinities. Knowledge-based scoring functions extract interaction frequencies or observed patterns from 3D structures of protein-ligand complexes to determine the likelihood of a new sample being from the data distribution.