In silico modeling of protein-ligand binding
The affinity of a drug to its target protein is one of the key properties of a drug. Although there are experimental methods to measure the binding affinity, they are expensive and relatively slow. Hence, accurately predicting this property with software tools would be very beneficial to drug discov...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/673579 |
| Acceso en línea: | http://hdl.handle.net/10803/673579 |
| Access Level: | acceso abierto |
| Palabra clave: | Docking Neural networks Explainable artificial intelligence Pharmacophore Modeling Acoplamiento molecular Redes neuronales Inteligencia artificial explicable Farmacóforo Modelización 577 |
| Sumario: | The affinity of a drug to its target protein is one of the key properties of a drug. Although there are experimental methods to measure the binding affinity, they are expensive and relatively slow. Hence, accurately predicting this property with software tools would be very beneficial to drug discovery. In this thesis, several applications have been developed to model and predict the binding mode of a ligand to a protein, to evaluate the feasibility of that prediction and to perform model interpretability in deep neural networks trained on protein-ligand complexes. |
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