Methods for Computer-aided Chemical Biology. Part 2: Evaluation of Compound Selectivity Using 2D Molecular Fingerprints

• Published in: Chemical biology & drug design 2007-09, Vol.70 (3), p.195-205
• Main Authors: Vogt, Ingo, Stumpfe, Dagmar, Ahmed, Hany E. A., Bajorath, Jürgen
• Format: Article
• Language: English
• Subjects: chemical biology; chemoinformatics; compound selectivity; Computational Biology - methods; computational methods; Drug Design; Image Processing, Computer-Assisted - methods; molecular fingerprints; similarity searching; Software Design; target families
• ISSN: 1747-0277; 1747-0285
• DOI: 10.1111/j.1747-0285.2007.00555.x

We analyze 558 compounds with selectivity against members of different protein families using two‐dimensional molecular fingerprint methods. The calculations target compounds selective for 13 targets belonging to three families. These compound sets were especially designed for selectivity studies. The identification of compounds displaying different selectivity patterns against related protein targets is a prerequisite for chemical genetics and genomics applications to specifically interfere with functions of individual members of protein families. Thus far, computational methods have only little impact on the search for selective compounds. This is in part due to the fact that selectivity is more difficult to study computationally than activity because selectivity analysis requires the evaluation of compounds binding to multiple targets. Here, we investigate the ability of state‐of‐the‐art two‐dimensional molecular fingerprints to detect compounds having different selectivity. The results of systematic similarity search calculations reveal that two‐dimensional fingerprints are capable of identifying compounds having different selectivity against closely related target proteins, although fingerprints were originally not developed for such applications. In addition to target‐selective molecules, fingerprints are also found to preferentially recognize compounds that are active at the target family level. Our findings suggest that similarity methods should merit further exploration in the study of compound selectivity across target families.