Resulting Clustering Medoids for: Molecular Dynamics and Machine Learning Give Insights on the Flexibility/Activity Relationships in Tyrosine Kinome

10 clustering-resulting medoids for each of the 43 simulated Tyrosine Kinases. The MD trajectories were clustered through the k-medoids algorithm, implemented in BiKi Life Sciences suite. For cluster generation, the RMSD matrix of the entire segment of the A-loop including the DFG-motif was used. The collected medoids could be well used as an atlas of conformations and pockets for virtual screening and docking campaigns. Each of the 43 Tyrosine Kinases of the study, in its active or/and inactive form, has been associated to its PDB id. Please, read the README_Tyrosine Kinome clustering medoids_20230712.txt file for further information.