CRRF-Score - Cumulative Ranking Random Forest Scoring Function for Free Energy of Binding Prediction in Protein-Ligand Docking

• Adriano Velasque Werhli FURG
• Patrícia Padula Lopes FURG
• Oscar E. Arrua FURG
• Andrej Aderhold FURG
• Karina dos Santos Machado FURG

Resumo

The growing challenges posed by infectious diseases, antimicrobial resistance, increasing cancer prevalence, and untreatable rare genetic conditions highlight the necessity to accelerate drug discovery. This process remains expensive and time-consuming but computational approaches like molecular docking combined with machine learning (ML) can impact on this process reducing costs and time. In molecular docking, the interaction between drug candidates (ligands) and target receptors is evaluated with Scoring Fuctions (SFs). The accurate prediction of the binding free energy in protein-ligand complexes performed by SFs remains a critical challenge in computational drug discovery. This study proposes CRRF-Score (Cumulative Ranking Random Forest Scoring Function), a ML-Based SF that integrates feature selection and ensemble models to enhance binding affinity prediction. CRRF-Score combines descriptors from DeltaVinaRF20, AutoDock Vina, and RDKit 2D, employing a diverse set of algorithms - Random Forest, Decision Tree, Lasso, Principal Component Analysis and Automatic Relevance Determination Regression for robust feature ranking. Our approach is trained on PDBbind 2018 and evaluated using CASF-2016 benchmarks, demonstrating competitive performance in Scoring Power and Ranking Power. Results show that our cumulative ranking strategy for feature selection combined with random forest algorithm for training the SF matches state-of-the-art commercial SFs in key metrics, offering a promising solution for molecular docking.