Fragment-similarity-based QSAR (FS-QSAR) algorithm for ligand biological activity predictions

Abstract

Quantitative structure–activity relationship (QSAR) studies are useful computational tools often used in drug discovery research and in many scientific disciplines. In this study, a robust fragment-similarity-based QSAR (FS-QSAR) algorithm was developed to correlate structures with biological activities by integrating fragment-based drug design concept and a multiple linear regression method. Similarity between any pair of training and testing fragments was determined by calculating the difference of lowest or highest eigenvalues of the chemistry space BCUT matrices of corresponding fragments. In addition to the BCUT-similarity function, molecular fingerprint Tanimoto coefficient (Tc) similarity function was also used as an alternative for comparison. For validation studies, the FS-QSAR algorithm was applied to several case studies, including a dataset of COX2 inhibitors and a dataset of cannabinoid CB2 triaryl bis-sulfone antagonist analogues, to build predictive models achieving average coefficient of determination (r ²) of 0.62 and 0.68, respectively. The developed FS-QSAR method is proved to give more accurate predictions than the traditional and one-nearest-neighbour QSAR methods and can be a useful tool in the fragment-based drug discovery for ligand activity prediction.

Keywords:

• fragment-similarity
• quantitative structure–activity relationship (QSAR)
• biological activity prediction
• BCUT descriptor
• fingerprint
• multiple linear regression
• COX2
• cannabinoid
• triaryl bis-sulfones

Acknowledgements

This work was supported by NIH grants R01 DA025612 and P50 GM067082.