| Activity Models of Cannabinoid Receptors

Shayma El Atawneh and Amiram Goldblum Ph.D.

Our laboratory developed a unique chemoinformatics classification method for modeling and discovery of novel drug candidates. We transform molecular 2D structures into a large set of physico-chemical properties, and use biological acitivy data from specific websites in order to construct “learning sets” that contain known active molecules which are “diluted” with a large number of inactive molecules. We focus mainly on “ligand based” modeling, which is not dependent on the structure of protein targets and are particularly useful if such target structures are absent1.

Iterative Stochastic Elimination (ISE)2 algorithm was devised to solve combinatorial “explosive” problems with frequently more than 10100 possible combinations. ISE identifies those variable values that contribute consistently to the worst results, but do not contribute similarly to the best solutions, and eliminates them thus producing a smaller set of variable values and a smaller number of combinatorial options, allowing one to proceed to the discovery of a set of optimal states of the system – an ensemble of filters of molecular properties (calculated by MOE software3, each filter composed of a few (4-5) molecular physicochemical properties and their ranges.  The final set of "best filters" solutions is considered to be a “model”.  For both receptors (CB1 and CB2), We construct models of agonism and of antagonism, each one separately, for both CB1 and CB2, based on resources of data such as ChEMBL4. These are used subsequently to score any molecule for its ability to have any type of interaction with CB1 and CB2, and thus to discover novel cannabinoids with specific activities.

 * Molecular Modeling and Drug Discovery Lab, Institute for Drug Research, The Hebrew University of Jerusalem, Israel

  1. Li, H. et al. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J. Pharm. Sci. 96, 2838–60 (2007).
  2. Stern, N. & Goldblum, A. Iterative Stochastic Elimination for Solving Complex Combinatorial Problems in Drug Discovery. Isr. J. Chem. 54, 1338–1357 (2014).
  3. Molecular Operating Environment (MOE), 2012.10; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2012.
  4. https://www.ebi.ac.uk/chembl/