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

(E-pub Ahead of Print)

Author(s): Said Byadi, Mouhi Eddine Hachim, Karima Sadik, Črtomir Podlipnik, Aziz Aboulmouhajir*.

Journal Name: Letters in Drug Design & Discovery

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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 (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 anti-cancer activity.

Keywords: Bcl-2 inhibitors, QSAR, kernel PLS, Validation, prediction, cancer.

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1570180817999200414155403
Price: $95