Development of CDK-targeted scoring functions for prediction of binding affinity
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Updated: Feb 14, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Nayara Maria Bernhardt Levin1, Val Oliveira Pintro2, Gabriela Bitencourt-Ferreira2
1Laboratory of Computational Systems Biology, School of Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil; Graduate Program in Cellular and Molecular Biology, The Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil.
Abstract:
Cyclin-dependent kinase (CDK) is an interesting biological macromolecule due to its role in cell cycle progression, transcription control, and neuronal development, to mention the most studied biological activities. Furthermore, the availability of hundreds of structural studies focused on the intermolecular interactions of CDK with competitive inhibitors makes possible to develop computational models to predict binding affinity, where the atomic coordinates of binary complexes involving CDK and ligands can be used to train a machine learning model. The present work is focused on the development of new machine learning models to predict binding affinity for CDK. The CDK-targeted machine learning models were compared with classical scoring functions such as MolDock, AutoDock 4, and Vina Scores. The overall performance of our CDK-targeted scoring function was higher than the previously mentioned scoring functions, which opens the possibility of increasing the reliability of virtual screening studies focused on CDK.
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