More Reliable Binding Affinity Prediction of Protein Ligands Combining Molecular Dynamics Simulations and Machine Learning Models
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Efficient and accurate prediction of protein-ligand binding affinities is essential for
advancing drug discovery. This thesis presents a novel approach that combines
molecular docking results with features derived from short molecular dynamics (MD)
simulations to enhance the prediction accuracy of binding affinities. The thesis
aims to bridge the efficiency of traditional docking methods with the more accurate
Free Energy perturbation (FEP) techniques. MD simulations were conducted on
a dataset of around 5,000 protein-ligand complexes and extracted features related
to binding enthalpy and dynamic behavior related to the more complex binding
entropy. These features, alongside molecular docking scores, were used to train machine
learning models. The CatBoost regressor was identified as the most effective
model, achieving better predictive accuracy than Molecular Docking alone. This
method facilitates more reliable binding affinity predictions by successfully integrating
docking insights with dynamic protein-ligand behavior, thereby accelerating the
early stages of drug discovery.
Keywords:
Beskrivning
Ämne/nyckelord
Protein-Ligand Binding, Binding Affinity Prediction, Molecular Docking, Molecular Dynamics (MD), Machine Learning, Binding Enthalpy, Binding Entropy, Drug Discovery, Free Energy Perturbation (FEP)