ProSAR: a new methodology for combinatorial library design.
A method is introduced for performing reagent selection for chemical library design based on topological (2D) pharmacophore fingerprints. Optimal reagent selection is achieved by optimizing the Shannon entropy of the 2D pharmacophore distribution for the reagent set. The method, termed ProSAR, is therefore expected to enumerate compounds that could serve as a good starting point for deriving a structure activity relationship (SAR) in combinatorial library design. This methodology is exemplified by library design examples where the active compounds were already known. The results show that most of the pharmacophores on the substituents for the active compounds are covered by the designed library. This strategy is further expanded to include product property profiles for aqueous solubility, hERG risk assessment, etc. in the optimization process so that the reagent pharmacophore diversity and the product property profile are optimized simultaneously via a genetic algorithm. This strategy is applied to a two-dimensional library design example and compared with libraries designed by a diversity based strategy which minimizes the average ensemble Tanimoto similarity. Our results show that by using the PSAR methodology, libraries can be designed with simultaneously good pharmacophore coverage and product property profile.
DECS GCS Computational Chemistry, AstraZeneca R&D Mölndal, Pepparedsleden 1, SE-43183 Mölndal, Sweden. hongming.chen@astrazeneca.com
Hongming
H
Chen
Ulf
U
Börjesson
Ola
O
Engkvist
Thierry
T
Kogej
Mats A
MA
Svensson
Niklas
N
Blomberg
Dirk
D
Weigelt
Jeremy N
JN
Burrows
Tim
T
Lange
D002264:Carboxylic Acids / Q000737:chemistry; D020650:Combinatorial Chemistry Techniques; D013329:Structure-Activity Relationship
D002264:Carboxylic Acids