PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12469/4466

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Browsing PubMed İndeksli Yayınlar Koleksiyonu by Publication Category "Kitap Bölümü - Uluslararası"

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    Computational Chemistry and Molecular Modeling of Reversible Mao Inhibitors
    (Humana Press Inc., 2023) Yelekçi, K.; Erdem, S.S.
    Proper elucidation of drug-target interaction is one of the most significant steps at the early stages of the drug development research. Computer-aided drug design tools have substantial contribution to this stage. In this chapter, we specifically concentrate on the computational methods widely used to develop reversible inhibitors for monoamine oxidase (MAO) isozymes. In this context, current computational techniques in identifying the best drug candidates showing high potency are discussed. The protocols of structure-based drug design methodologies, namely, molecular docking, in silico screening, and molecular dynamics simulations, are presented. Employing case studies of safinamide binding to MAO B, we demonstrate how to use AutoDock 4.2.6 and NAMD software packages. © 2023, The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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    Hands-On Docking With Molegro Virtual Docker
    (Humana Press Inc., 2026) Dere, D.; Pehlivan, S.N.; da Silva, A.D.; de Azevedo Junior, W.F.
    Molegro Virtual Docker (MVD) integrates state-of-the-art search algorithms and scoring functions dedicated to protein-ligand docking simulations. It implements differential evolution as a search engine and MolDock and Plants scores to calculate binding affinity. In this work, we describe a workflow focused on how to build regression models to predict the inhibition of cyclin-dependent kinase 2 (CDK2). We employ available structural and binding data to construct machine learning models to calculate CDK2 inhibition based on the atomic coordinates obtained through docking simulations performed with MVD. We present a hands-on approach to show how to integrate docking results and machine learning methods available at Scikit-Learn to build targeted scoring functions. Our regression models show superior predictive performance compared with classical scoring functions. All CDK2 datasets and Jupyter Notebooks discussed in this work are available at GitHub: https://github.com/azevedolab/docking#readme. We made the source code of the program SAnDReS 2.0 available at https://github.com/azevedolab/sandres. © 2025 Elsevier B.V., All rights reserved.