Matched molecular pairs as a medicinal chemistry tool.

By E. Griffen, Andrew G. Leach, G. Robb, Daniel J. Warner. 2011

256 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Ligand biological activity predicted by cleaning positive and negative chemical correlations (2019, positive): “Those “matched molecular pairs” (43) were experimentally tested for M1 agonist activity.”
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (2018, methodology): “Our approach is closely related to matched molecular pair analysis (MMPA) (Griffen et al., 2011; Dossetter et al., 2013) in drug de novo design, where the matched pairs are hard-coded into graph transformation rules.”
Rethinking drug design in the artificial intelligence era (2019, background): “…complexity and dimensionality of modern drug discovery project datasets (and to translate their findings into new compounds), scientists often have to resort to simple heuristics such as rules of thumb, efficiency metrics 140 , model systems like log P or log D , or matched molecular pairs 141 .”
Artificial intelligence in drug discovery: recent advances and future perspectives (2021, methodology): “Contemporary approaches are based on applying a given set of molecular transformations for optimization, such as matched molecular pairs [114,115], or rules-of-thumb for functional group and molecular framework modification [116].”