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Additional info for 18th European Symposium on Quantitative Structure – Activity Relationships
Instead of using only one global model (classical approach), we use weighted local models for predictions of query instances dependent on cluster memberships. The obtained clusterings are overlapping and non-exhaustive. In other words, a compound can be member of more than one cluster or member of no cluster at all. The dataset is divided into several parts representing the different structural classes. Compared to global models that generally try to capture everything at once, local models are high-quality models of small regions of the chemical space that often have the advantage of being much more interesting and understandable to a domain expert.
2] A. G. D. Jones, D. A. Cosgrove, P. W. Kenny, L. Ruston, P. MacFaul, J. M. Colclough, B. Law, J. Med. Chem. 2006, 49, 6672. 58 18th European Symposium on Quantitative Structure-Activity Relationships ORAL PRESENTATION 6 BIOPHYSICS-BASED LIBRARY DESIGN: DISCOVERY OF “NON-ACID” INHIBITORS OF S1 DHFR Veer Shanmugasundaram*, Kris Borzilleri, Jeanne Chang, Boris Chrunyk, Mark Flanagan, Seungil Han, Melissa Harris, Brian Lacey, Richard Miller, Parag Sahasrabudhe, Ron Sarver, Holly Soutter, Jane Withka Antibacterials Chemistry/Discovery Technologies, Pfizer PharmaTherapeutics Research & Development, Groton, CT - 06333, USA Methicillin-resistant Staphylococcus aureus (MRSA), the causative agent of many serious nosocomial and community acquired infections, and other gram-positive organisms can show resistance to trimethoprim (TMP) through mutation of the chromosomal gene or acquisition of an alternative DHFR termed "S1 DHFR" To develop new therapies for health threats such as MRSA, it is important to understand the molecular basis of TMP resistance and use that knowledge to design and develop novel inhibitors that are effective against S1 DHFR.
Indeed, our experience suggests that discovery teams are more interested in practical tools that give them convenient access to all their relevant project data and allow them to visualize the underlying multi-dimensional SAR in ways that make it easy to identify general trends and exceptions. Here, we present a family of three closely-related tools, scaffold explorer, multi-dimensional SAR maps, and single R-group polymorphisms (SRP’s) or R-cliffs, which help the medicinal chemists organize, understand, and exploit SAR.