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 (kernel-based 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: Models based on binary fingerprints with two kPLS factors have been found with decent predictive power
( ), while the optimal number of factors is about 5. The enrichment study (148 actives, 5700 decoys) has shown the
excellent classification ability of our models (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. Promising results obtained, methods, and applications highlighted
in this study will help us to design more selective Bcl-2 inhibitors with better structural characteristics and improved