Background: Bcl-2 family plays an essential role in the cell cycle events incorporating survival, proliferation, and differentiation in normal and neoplastic neuronal cells. Thus, it has been validated as a principal target for the treatment of cancer. For this reason, we will build a model based on a large number of Bcl-2 inhibitors to predict the activities of new compounds as future Bcl-2 inhibitors.
Methods: In this study, QSAR models were successfully used to predict the inhibitory activity against Bcl-2 for a set of compounds collected from BDB (Binding database). The kPLS (kernelbased Partial Least-Square) method implemented in Schrodinger's Canvas, was used for searching the correlation between pIC50 and binary fingerprints for a set of known Bcl-2 inhibitors.
Results and Discussion: Models based on binary fingerprints with two kPLS factors have been found with decent predictive power (q2 > 0.58), while the optimal number of factors is about 5. The enrichment study (148 actives, 5700 decoys) has shown excellent classification ability of our models (AUC > 0.90) for all cases).
Conclusion: We found that the kPLS method, in combination with binary fingerprints, is useful for the affinity prediction and the Bcl-2 inhibitors classification. The obtained promising results, methods, and applications highlighted in this study will help us to design more selective Bcl-2 inhibitors with better structural characteristics and improved anti-cancer activity.
[http://dx.doi.org/10.1073/pnas.95.10.5724] [PMID: 9576951]
[http://dx.doi.org/10.1021/jm101181u] [PMID: 21235240]
[http://dx.doi.org/10.1021/jm300608w] [PMID: 22747598]
[http://dx.doi.org/10.1021/jm3010306] [PMID: 23030453]
[http://dx.doi.org/10.1038/nm.3048] [PMID: 23291630]