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Cherkasov A.,University of British Columbia | Muratov E.N.,University of North Carolina at Chapel Hill | Muratov E.N.,Ukrainian Academy of Sciences | Fourches D.,University of North Carolina at Chapel Hill | And 18 more authors.
Journal of Medicinal Chemistry | Year: 2014

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making. © 2013 American Chemical Society.


Damewood Jr. J.R.,Astrazeneca | Lemian C.L.,Astrazeneca | Masek B.B.,Tripos Inc.
Journal of Chemical Information and Modeling | Year: 2010

NovoFLAP is a computer-aided de novo design tool that generates medicinally relevant ideas for ligand- based projects. The approach combines an evolutionary algorithm (EA-Inventor) with a powerful ligand- based scoring function that uses both molecular shape and pharmacophore features in a multiconformational context (FLAP). We demonstrate that NovoFLAP can generate novel ideas that are not only appealing to design scientists but are also validated by comparison to compounds known to demonstrate activity at the desired biological target. NovoFLAP provides a novel computer-aided design technique that can be used to generate ideas that maintain desirable molecular attributes, such as activity at the primary biological target, while offering opportunities to surmount additional design challenges. Application to the design of the first nonbasic 5HT 1B antagonist is presented. © 2010 American Chemical Society.


Cramer R.D.,Tripos Inc.
Journal of Computer-Aided Molecular Design | Year: 2012

QSAR approaches, including recent advances in 3D-QSAR, are advantageous during the lead optimization phase of drug discovery and complementary with bioinformatics and growing data accessibility. Hints for future QSAR practitioners are also offered. © 2011 The Author(s).


Cramer R.D.,Tripos Inc.
Journal of Computer-Aided Molecular Design | Year: 2011

The average error of pIC50 prediction reported for 140 structures in make-and-test applications of topomer CoMFA by four discovery organizations is 0.5. This remarkable accuracy can be understood to result from a topomer pose's goal of generating field differences only at lattice intersections adjacent to intended structural change. © Springer Science+Business Media B.V. 2010.


PubMed | Tripos Inc.
Type: Journal Article | Journal: Expert opinion on drug discovery | Year: 2013

Structural novelty has depended for its discovery on a combination of experimental and virtual screening, where virtual screening has meant either docking into a receptor or pharmacophoric or two-dimensional similarity. In this review, leadhopping denotes a new, far more convenient and apparently quite as effective, set of virtual screening methods, all of which emphasise a much more detailed similarity in ligand shape than the pharmacophore approach. Furthermore, some of these leadhopping methods have been adapted to address much broader needs, such as accelerated and simplified lead optimisation, access to an unprecedented vast structural space, and even useful forecasts of off-target pharmacological effects in humans. These methods seem robust and automatable enough to be used directly by laboratory chemists.

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