Background: The multidrug transporter P-glycoprotein (P-gp) ATP-binding cassette B1 (ABCB1) is one of the key proteins influencing bioavailability and uptake of drugs in the brain. In addition, it is one of the main factors contributing to multidrug resistance in tumor therapy. Due to its promiscuous substrate recognition, prediction of substrate properties for the multidrug transporter P-gp represents a challenging task.
Results: Here, we present data on three classification methods of ABCB1 substrates and nonsubstrates based on 2D and 3D shape similarity calculations with special emphasis on the use of the similarity-based relationship approach. The results indicate that a reference set structurally similar to the data set performs superiorly to those selected on the basis of maximum diversity and suggests Random Forest as the most suitable classification method for this data set.
Conclusion: This study suggests 2D descriptors representing 3D features best suited to the classification of P-gp substrates and nonsubstrates.
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