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<https://discovery.ucl.ac.uk/id/eprint/10191295> <http://purl.org/dc/terms/title> "Calculating Absolute Ligand Binding Free Energies and Ranking Ligand Binding Poses Using Metadynamics enabled Molecular Dynamics Simulations"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10191295> <http://purl.org/ontology/bibo/abstract> "The work presented in this thesis focuses on the use of molecular dynamics (MD) and enhanced sampling methods for investigating ligand binding poses and determining protein-ligand binding affinity. Good pre diction of the arrangement of these complexes and their strength is crucial for successful structure based drug design (SBDD) efforts so this thesis makes a significant contribution in furthering the use of computational tools in SBDD. First, chapter 3 presents OpenBPMD, an open-source Python re implementation of binding pose metadynamics (BPMD), a MD-based tool for ranking ligand poses from a set of candidates derived from dock ing. The role of accurate water positioning on the performance of the algorithm is also investigated, showed how the combination with a grand canonical Monte Carlo algorithm improves the accuracy of the predic tions. Then chapter 4 explains how the funnel metadynamics (fun-metaD) algorithm was implemented on a high-performance MD engine, OpenMM. This implementation was validated on host-guest systems. Afterwards a larger data set is interrogated, examining the effects on host-guest bind ing by varying the water model (TIP3P, OPC3 and OPC) and the partial atomic charge assignment methods, AM1-BCC and RESP. Finally, chapter 5 investigates the binding of fragment-like ligands in three different protein targets by applying fun-metaD. Advancements are made on funnel-shaped restraint automation and a new set of collective variables (CV) is tested as well. However, a lack of convergence due to an excess of metadynamic bias and missing slow degrees of freedom is observed. In order to address these issues, chapter 6 delves into apply ing a neural network-based CV, called Deep-LDA, and a novel enhanced sampling algorithm, termed on-the-fly probability-enhanced sampling. Although smooth converging, some issues in pose discrimination still re main."^^<http://www.w3.org/2001/XMLSchema#string> .
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