Assessing How Well a Modeling Protocol Captures a Structure-Activity Landscape

We introduce the notion of structure-activity landscape index (SALI) curves as a way to assess a model and a modeling protocol, applied to structure-activity relationships. We start from our earlier work [ J. Chem. Inf. Model., 2008, 48, 646-658], where we show how to study a structure-activity relationship pairwise, based on the notion of "activity cliffs"--pairs of molecules that are structurally similar but have large differences in activity. There, we also introduced the SALI parameter, which allows one to identify cliffs easily, and which allows one to represent a structure-activity relationship as a graph. This graph orders every pair of molecules by their activity. Here, we introduce the new idea of a SALI curve, which tallies how many of these orderings a model is able to predict. Empirically, testing these SALI curves against a variety of models, ranging over two-dimensional quantitative structure-activity relationship (2D-QSAR), three-dimensional quantitative structure-activity relationship (3D-QSAR), and structure-based design models, the utility of a model seems to correspond to characteristics of these curves. In particular, the integral of these curves, denoted as SCI and being a number ranging from -1.0 to 1.0, approaches a value of 1.0 for two literature models, which are both known to be prospectively useful.

[1]  A. Tropsha,et al.  Beware of q 2 , 2002 .

[2]  Keith Abe,et al.  Identification of orally active, potent, and selective 4-piperazinylquinazolines as antagonists of the platelet-derived growth factor receptor tyrosine kinase family. , 2002, Journal of medicinal chemistry.

[3]  S. Wold,et al.  Comparative molecular field analysis , 1991 .

[4]  Carlos R Rodrigues,et al.  Structure-activity relationships of the antimalarial agent artemisinin. 6. The development of predictive in vitro potency models using CoMFA and HQSAR methodologies. , 2002, Journal of medicinal chemistry.

[5]  H. Kubinyi.  Drug research: myths, hype and reality , 2003, Nature Reviews Drug Discovery.

[6]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[7]  C. E. Peishoff,et al.  A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.

[8]  Rajarshi Guha,et al.  Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues , 2004, J. Chem. Inf. Model..

[9]  Zhiliang Li,et al.  Approach to Estimation and Prediction for Normal Boiling Point (NBP) of Alkanes Based on a Novel Molecular Distance-Edge (MDE) Vector , 1998, J. Chem. Inf. Comput. Sci..

[10]  Boris Mirkin,et al.  A Measure of Domain of Applicability for QSAR Modelling Based on Intelligent K-Means Clustering , 2007 .

[11]  P. Jurs,et al.  Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors. , 2005 .

[12]  Rajarshi Guha,et al.  Structure-Activity Landscape Index: Identifying and Quantifying Activity Cliffs , 2008, J. Chem. Inf. Model..

[13]  T. Halgren,et al.  A priori prediction of activity for HIV-1 protease inhibitors employing energy minimization in the active site. , 1995, Journal of medicinal chemistry.

[14]  Jeffrey J. Sutherland,et al.  Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure-Activity Relationships , 2003, J. Chem. Inf. Comput. Sci..

[15]  John Saunders,et al.  Pyrrolidinones as orally bioavailable antagonists of the human melanocortin-4 receptor with anti-cachectic activity. , 2007, Bioorganic & medicinal chemistry.

[16]  J. V. Drie.  Pharmacophore Discovery: A Critical Review , 2003 .

[17]  P. Charifson,et al.  Are free energy calculations useful in practice? A comparison with rapid scoring functions for the p38 MAP kinase protein system. , 2001, Journal of medicinal chemistry.

[18]  Ulf Norinder,et al.  Single and domain mode variable selection in 3D QSAR applications , 1996 .

[19]  Robert P. Sheridan,et al.  Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR , 2004, J. Chem. Inf. Model..

[20]  H. Kubinyi,et al.  Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. , 1998, Journal of medicinal chemistry.

[21]  Gerald M. Maggiora,et al.  On Outliers and Activity Cliffs-Why QSAR Often Disappoints , 2006, J. Chem. Inf. Model..

[22]  John D. Walker,et al.  Qsars promote more efficient use of chemical testing resources—Carpe diem , 2003, Environmental toxicology and chemistry.

[23]  Rajarshi Guha,et al.  Determining the Validity of a QSAR Model - A Classification Approach , 2005, J. Chem. Inf. Model..

[24]  C. Steinbeck,et al.  Recent developments of the chemistry development kit (CDK) - an open-source java library for chemo- and bioinformatics. , 2006, Current pharmaceutical design.

[25]  David T. Stanton,et al.  Development and Use of Hydrophobic Surface Area (HSA) Descriptors for Computer‐Assisted Quantitative Structure—Activity and Structure—Property Relationship Studies. , 2004 .

[26]  Yi Li,et al.  In silico ADME/Tox: why models fail , 2003, J. Comput. Aided Mol. Des..

[27]  Beth A. Fleck,et al.  Pyrrolidines as potent functional agonists of the human melanocortin-4 receptor. , 2007, Bioorganic & medicinal chemistry letters.

[28]  A. Cavalli,et al.  Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K(+) channel blockers. , 2002, Journal of medicinal chemistry.

[29]  N. Kruhlak,et al.  Estimating the safe starting dose in phase I clinical trials and no observed effect level based on QSAR modeling of the human maximum recommended daily dose. , 2004, Regulatory toxicology and pharmacology : RTP.