title: A comparative QSAR study using CoMFA, HQSAR, and FRED/SKEYS paradigms for estrogen receptor binding affinities of structurally diverse compounds.
abstract: The three-dimensional quantitative structure-activity relationship (QSAR) technique of comparative molecular field analysis (CoMFA) has demonstrated the ability to provide accurate predictions for diverse chemical compounds when trained with molecules of diverse chemical type. Although predictive, the derivation and utilization of models of this type are quite computationally and person power intensive. It is this intensity that pragmatically limits the widespread implementation of these models as predictive tools. In this study, two newer QSAR techniques were evaluated as possible alternatives to CoMFA based QSAR models for the purpose of rapidly identifying estrogen receptor ligands from diverse collections of molecules. The first of these is Hologram QSAR, or HQSAR. HQSAR utilizes Tripos molecular fingerprints as descriptors in conjunction with partial least squares (PLS) regression and cross-validation routines. The HQSAR technique demonstrated the ability to rapidly develop QSAR models independent of the intense user input (i.e. geometry optimization, conformational analysis, and molecular superposition were not required). Second, a newly developed QSAR paradigm that utilizes Molecular Design Limited (MDL) substructure keys (SKEYS) as descriptors in combination with an evolutionary algorithm, Fast Random Elimination of Descriptors (FRED), was evaluated. By utilizing the FRED/SKEYS algorithm, a simple substructure-based QSAR model was derived that was comparable in statistical robustness and predictive ability to both CoMFA and HQSAR derived models. A comparison of the utility of these three approaches as computational tools for the rapid identification of estrogen receptor ligands as potential endocrine disruptors as assessed by model predictive ability will be described.
authors.affiliation: Pfizer Global Research and Development, Ann Arbor, Michigan 48105, USA. Chris.waller@pfizer.com
authors.forename: Chris L
authors.initials: CL
authors.lastname: Waller
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D015195:Drug Design; D004967:Estrogens / Q000737:chemistry / Q000494:pharmacology; D004968:Estrogens, Non-Steroidal / Q000737:chemistry / Q000494:pharmacology; D016018:Least-Squares Analysis; D008024:Ligands; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D011897:Random Allocation; D011960:Receptors, Estrogen / Q000187:drug effects; D015203:Reproducibility of Results
chemical_list: D004967:Estrogens; D004968:Estrogens, Non-Steroidal; D008024:Ligands; D011960:Receptors, Estrogen