All molecular similarity methods exploit the so-called similarity principle, which states that "structurally similar molecules are likely to have similar biological and pharmacological properties" [51].
We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search.
By comparing empirical data obtained from cross-reactivity studies with the molecular modeling studies, our published data indicate that 2D molecular similarity methods perform well in predicting cross-reactivity of drugs to existing drug of abuse screening immunoassays.
Previously we have shown in several studies that 2D molecular similarity methods perform well in predicting cross-reactivity of drugs to existing drug of abuse screening immunoassays [44, 47, 49].
The lack of a more noticeable difference between the NN pairs and random pairs can be attributed to an assumption used in the molecular similarity methods and the nature of typical biological assays.
Various computational approaches have been proposed for the prediction of compound toxicity ranging from quantitative structure activity relationship modeling to molecular similarity-based methods and machine learning.
Additionally, we have formulated novel quantitative molecular similarity analysis (QMSA) methods using mathematical invariants.
Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established.
3D-QSAR studies of some tricyclicpiperazinyl derivatives as farnesyltransferase inhibitors were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds.
Based on the binding conformations and their alignment inside the binding pocket of CCR5, three-dimensional structure activity relationship (3D QSAR) analyses were performed on these antagonists using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods.
