Activity landscape analysis, CoMFA and CoMSIA studies of pyrazole CB1 antagonists

Computational studies on N-phenyl pyrrole derivatives as MmpL3 inhibitors in Mycobacterium tuberculosis

The fight against tuberculosis (TB) is a time immemorial one and the emergence of new drug resistant strains of Mycobacterium tuberculosis keeps throwing new challenges to the scientific community immersed in finding mechanisms to control this dreaded disease. Computer aided drug designing (CADD) is one of the several approaches that can assist in identifying the potent actives against Mycobacterium. In this work, a series of 109 known Mycobacterial membrane proteins large 3 (MmpL3) inhibitors were pooled and atom based 3D QSAR analysis was performed to understand the structural features essential for inhibitory activity against the MmpL3, known to be a key player in transporting substances critical for cell wall integrity of Mycobacterium. The data set employed was randomly split into training set and test set molecules. The training set of 74 molecules was used to derive CoMFA and CoMSIA models that were statistically reliable (CoMFA: q²_loo = 0.53; r²_ncv = 0.93 and CoMSIA: q²_loo = 0.60; r²_ncv = 0.93). The derived models also exhibited good external predictive ability (CoMFA: r²_pred = 0.78 and CoMSIA: r²_pred = 0.79). The results are quite encouraging and information derived from these analyses was applied to design new molecules. The designed molecule showed appreciable predicted activity values and reasonably good ADMET profile. The strategy used in designing new molecules can be pursued in the hunt for new chemical entities targeting MmpL3, expanding the existing arsenal against TB.

Activity cliffs and activity cliff generators based on chemotype-related activity landscapes

Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized. Compounds in each database were grouped into chemotype classes. Then, pairwise comparisons of structure similarities and activity differences were calculated for each compound and used to construct chemotype-based structure-activity similarity (SAS) maps. Different landscape distributions among four major regions of the SAS maps were observed for different subsets of molecules grouped in chemotypes. Based on this observation, the activity cliff enrichment factor was calculated to numerically detect chemotypes enriched in activity cliffs. Several chemotype classes were detected having major proportion of activity cliffs than the entire database. In addition, some chemotype classes comprising compounds with smooth structure activity relationships (SAR) were detected. Finally, the activity cliff generator concept was applied to compounds grouped in chemotypes to extract valuable SAR information.