RFL-Score: Random Forest with Lasso Scoring Function for Protein-Ligand Molecular Docking
Oscar E. Arrua, Andrej Aderhold, Adriano Velasque Werhli, Karina Machado · 2024
Due to the growth computational power and easy access to vast amounts of biological data, machine learning (ML) models are being increasingly considered within the field of Rational Drug Design (RDD). In RDD, molecular docking methods are employed to predict the optimal binding mode of a small molecule (ligand) in the binding site of a target receptor (typically a protein). Molecular docking software utilizes Scoring Functions (SFs) to estimate the protein-ligand binding affinity. These SFs can be classified according to their development methodology: Physics-based, Empirical, Knowledge-based, and ML-based. ML-based SFs are trained using features extracted from protein-ligand complexes with known experimental affinities, relying heavily on the attributes used during training. Both the input data and the selected features can significantly impact the accuracy of the SFs. Additionally, different ML algorithms can be applied to select the most appropriate features and propose the SF model. In this study we propose RFL-Score, a novel ML-based SF using Random Forest with Lasso regression. RFL-Score is a ready-to-use SF developed with a straightforward methodology and trained exclusively on open-access biomolecular data. Only open-source software was employed for extracting features from the proteins, ligands, and complexes. The proposed SF was evaluated using the well-established CASF-2016 benchmark achieving outstanding performance compared to other SFs. The RFL-Score scoring function and elements used for training are freely available on GitHub at https://github.com/rflscore.