Eric J. Martin

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Screening synthetic combinatorial libraries, such as mixtures of oligo(N-substituted)glycines, facilitates rapid drug lead discovery and optimization by vastly increasing the number of candidate molecules made and tested. Discovery efficiency and productivity can be further improved by using experimental design to maximize molecular diversity for a given(More)
Screening a diverse, combinatorial library of ca. 5000 synthetic dimer and trimer N-(substituted)glycine "peptides" yielded novel, high-affinity ligands for 7-transmembrane G-protein-coupled receptors. The peptoid library was efficiently assembled using readily available chemical building blocks. The choice of side chains was biased to resemble known(More)
In this chapter we review the use of 3-D pharmacophores in drug discovery. Recent advances are highlighted, including the application of pharmacophore descriptors generated both from ligands and protein binding sites. The application of 3-D pharmacophore fingerprints as molecular descriptors for similarity and diversity applications such as virtual(More)
Combinatorial library design attempts to choose the best set of substituents for a combinatorial synthetic scheme to maximize the chances of finding a useful compound, such as a drug lead. Initial efforts were focused primarily on maximizing diversity, perhaps allowing some bias by the inclusion of a small, fixed set of pharmacophoric substituents. However,(More)
Reliable in silico prediction methods promise many advantages over experimental high-throughput screening (HTS): vastly lower time and cost, affinity magnitude estimates, no requirement for a physical sample, and a knowledge-driven exploration of chemical space. For the specific case of kinases, given several hundred experimental IC(50) training(More)
Structure-based virtual screening followed by selection of a top fraction of the rank-ordered result list suffers from many false positives and false negatives because the general scoring functions are not accurate enough. Many approaches have emerged to address this problem by including knowledge about the specific target in the scoring and selection(More)
Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound(More)
New approaches for combinatorial library design and molecular diversity analysis have been developed by extending previous work from the fields of quantitative structure-activity relationship, computational chemistry, and chemical information. Recent work has begun to address design efficiency and validation of descriptors for combinatorial library design.
It has been notoriously difficult to develop general all-purpose scoring functions for high-throughput docking that correlate with measured binding affinity. As a practical alternative, AutoShim uses the program Magnet to add point-pharmacophore like "shims" to the binding site of each protein target. The pharmacophore shims are weighted by partial(More)