Structure-Activity Relationships and Networks: A Generalized Approach to Exploring Structure-Activity Landscapes

GRAND Seminar 12:00 noon, March 29, Tue., 2011, ENGR 4201

Rajarshi Guha
http://www.rguha.net/
Research Scientist
NIH Chemical Genomics Center

Host:

Huzefa Rangwala

Abstract:

Activity cliffs are pairs of molecules that are structurally very similar, yet exhibit very different activities. With this definition one can view molecules and their activities in terms of a landscape - consisting of smooth rolling hills (similar molecules with similar activities) and jagged cliffs. The landscape view provides a framework within which one can analyse structure-activity relationship (SAR) models. The first step is to numerically characterize the landscape and we have devised the Structure Activity Landscape Index (SALI) to do this. This value can be used to construct a network model of the dataset, that allows one to interactively explore activity cliffs of varying degree. While a useful and intuitive visualization of SAR's, the SALI allows us to go further and quantitatively assess the ability of models to encode the SAR's present in a dataset. I will highlight discuss an extension of the SALI, termed SALI curves, to determine how well an SAR model has been able to encode the landscape. I will highlight its generality by applying it to QSAR, docking and CoMFA models. I will also briefly describe the utility of the SALI values to assess the suitability of a given molecular represention for predictive modeling. Finally, I will dicuss more recent work on the predictive model of SALI values, as a way to extend a structure activity landscape as well as prioritize new molecules as part of putative activity cliffs

Bio:

Rajarshi Guha is a Research Scientist at the NIH Chemical Genomics Center, working on cheminformatics and bioinformatics problems in high throughput screening. Recently, he has developed the informatics infrastructure for the Trans-NIH RNAi Screening Initiative and is interested in developing strategies to integrate small molecule and RNAi screening data generated using high-content methods. He also holds an adjunct professorship at Indiana University in the School of Informatics and is the Chair-Elect of the ACS Division of Chemical Information.