Chemical transformations that yield compounds with distinct activity profiles.

We have systematically searched for chemical changes that generate compounds with distinct biological activity profiles. For this purpose, activity profiles were generated for ∼42000 compounds active against human targets. Unique activity profiles involving multiple target proteins were determined, and all possible matched molecular pairs (MMPs) were identified for compounds representing these profiles. An MMP is defined as a pair of compounds that are distinguished from each other only at a single site such as an R group or ring system. For example, in an MMP, a hydroxyl group might be replaced by a halogen atom or a benzene ring by an amide group. From ∼37500 MMPs, more than 300 nonredundant chemical transformations were isolated that yielded compounds with distinct activity profiles. None of these transformations was found in pairs of compounds with overlapping activity profiles. These transformations were ranked according to the number of MMPs, the number of activity profiles, and the total number of targets that they covered. In many instances, prioritized transformations involved ring systems of varying complexity. All transformations that were found to switch activity profiles are provided to enable further analysis and aid in compound design efforts.

[1]  Robert D Clark,et al.  Neighborhood behavior: a useful concept for validation of "molecular diversity" descriptors. , 1996, Journal of medicinal chemistry.

[2]  A. Hopfinger,et al.  Methods for applying the quantitative structure-activity relationship paradigm. , 2004, Methods in molecular biology.

[3]  Jens Sadowski,et al.  Structure Modification in Chemical Databases , 2005 .

[4]  Xin Wen,et al.  BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities , 2006, Nucleic Acids Res..

[5]  P. Hajduk,et al.  Statistical analysis of the effects of common chemical substituents on ligand potency. , 2008, Journal of Medicinal Chemistry.

[6]  Mathias Wawer,et al.  Navigating structure-activity landscapes. , 2009, Drug discovery today.

[7]  Ian A. Watson,et al.  Rationalizing Lead Optimization by Associating Quantitative Relevance with Molecular Structure Modification , 2009, J. Chem. Inf. Model..

[8]  J. Bajorath,et al.  Chemical Substitutions That Introduce Activity Cliffs Across Different Compound Classes and Biological Targets , 2010, J. Chem. Inf. Model..

[9]  Visakan Kadirkamanathan,et al.  Lead Optimization Using Matched Molecular Pairs: Inclusion of Contextual Information for Enhanced Prediction of hERG Inhibition, Solubility, and Lipophilicity , 2010, J. Chem. Inf. Model..

[10]  Jameed Hussain,et al.  Computationally Efficient Algorithm to Identify Matched Molecular Pairs (MMPs) in Large Data Sets , 2010, J. Chem. Inf. Model..

[11]  J. Bajorath,et al.  Activity landscape representations for structure-activity relationship analysis. , 2010, Journal of medicinal chemistry.

[12]  Anne Mai Wassermann,et al.  Design of Multitarget Activity Landscapes That Capture Hierarchical Activity Cliff Distributions , 2011, J. Chem. Inf. Model..