Indirect Similarity Based Methods for Effective Scaffold-Hopping in Chemical Compounds

Abstract

Methods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods that are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. The set of techniques that are presented capture these indirect similarities using approaches based on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that most of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.

Automatic Tailoring and Transplanting: A Practical Method that Makes Virtual Screening More Useful

“…It is also similar in concept to "scaffold hopping". 48,49 The value of our AutoT&T method is that it automates this process with Table 4. New Designs Generated by LUDI in the Three Test Cases a In each structure, the fragments in black are reserved during optimization; whereas those in red are newly added fragments.…”

Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods

“…These descriptors can be based on physiochemical properties as well as topological and geometric substructures (fragments) 30,36-40. In this study we use the ECFP descriptors37,38 as it has been shown to be very effective in the context of classification, ranked-retrieval, and scaffold-hopping 39,41. We utilize Scitegic’s Pipeline Pilot42 to generate ECFP4, a variation of ECFP descriptor-space for our dataset.…”

Efficient identification of Tanimoto nearest neighbors

“…We encode each chemical compound as a sparse frequency vector of the molecular fragments it contains, represented by GF [42] descriptors extracted using the AFGen v. 2.0 [43] program. 3 AFGen represents molecules as graphs, with vertices corresponding to atoms and edges to bonds in the molecule.…”