Fingerprint-based 2D-QSAR Models for Predicting Bcl-2 Inhibitors Affinity

Abstract

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.