Solutions Case Studies
Download DocumentMDL Databases Assist with Identifying Drug-Like Compounds
"We wanted to have for our analysis one good, diverse compound set that was also being used widely by many companies. Both ACD and ACD-SC were good choices for us, as other compound sources in our opinion did not have the type of selection we were looking for."
Kal Ramnarayan, Vice President and Chief Scientific Officer
Structural Bioinformatics, Inc.
Structural Bioinformatics' multilevel chemical compatibility (MLCC) calculation is the last component in a series of proprietary lead generation technologies that the company has developed. The other technologies provide a database of genomic-derived structural information (SDdBase), templating technology that uses protein structure to create virtual constructs of protein pharmacophores (DynaPharms), and a comprehensive library of virtual conformers (CombiLib).
On the U.S. public television program for children, Sesame Street, a catchy tune exhorts children to identify similarities and differences among objects: "One of these things is not like the other..." Two researchers at Structural Bioinformatics, Inc., (San Diego, CA) have modified this query for an older, more educated audience. Their paper, published in last October's issue of The Journal of Combinatorial Chemistry, offers a computational method for determining which compounds are most like drugs.
Authors Jing Wang and Kal Ramnarayan developed multilevel chemical compatibility (MLCC) calculation as a way to measure the drug-like character of a compound. The approach goes beyond substructure and similarity assessments to consider what the authors call "local structures"—the local chemical environment surrounding each atom or group of atoms in a molecule. Compounds are considered "drug-like" when their atoms and groups are situated in similar environments to those found in existing drugs.
To read the complete case study, click the Download Document link near the top of the this article.
