Past Research


Washington University in St. Louis, St. Louis, MO, 2008-2015

Computational Chemistry Methods (MOE, OpenEye Software)
New approaches for structure-based virtual screening

Project Modeling (MOE, OpenEye Software, AMBER)
Collaboration on a variety of therapeutic and basic science research projects with investigators from many departments at Washington University in St. Louis as well as from other institutions

High-Throughput Screening Core
Managed the High-Throughput Screening Core (HTSC) and oversaw the combination of the CGSC and HTC. This effort laid the groundwork for the creation of the current Center for Drug Discovery.

Pfizer Global Research & Development, Cambridge, MA and Ann Arbor, MI, 2001 - 2008

Computational Chemistry (MOE, Maestro, Jaguar, Glide, Gold)
Structure-based and ligand-based modeling for early-discovery therapeutic projects including HTS triage, protein construct design, virtual screening, homology modeling, fragment screening, docking and scoring, tautomer identification, compound and library design, 2D and 3D QSAR, ADME modeling and selectivity prediction

Structural Bioinformatics (MOE, Sybyl, LOOK, Biopendium, ClustalW)
Target analysis for exploratory projects involving homology modeling, sequence analysis and protein construct design for NMR and crystallography

E.B.Fauman, S.C.Guru, A.R.Johnson, S.A.Wildman Expression of Mutant TACE Catalytic Domain. PCT Int. Appl. WO2005080560A1, 2005

Novel Methods Development
Small-molecule superposition
Protein-ligand docking (additional publication in preparation)
QSAR Modeling: searching descriptor and algorithm space for optimal combinations
Virtual screening and library design for large compound sets

S.A.Wildman and R.V.Stanton Using atomic property values for the selection of small-molecule superposition. 230th ACS Meeting, Washington DC, 2005

S.Wittkopp, J.E.Penzotti, R.V.Stanton and S.A.Wildman Knowledge-based docking for kinases with minimal bias. 234th ACS Meeting, Boston, MA, 2007

S.A.Wildman and R.V.Stanton Finding the best protocol for enezyme activity modeling. 234th ACS Meeting, Boston, MA, 2007

S.Sciabola, R.V.Stanton, S.Wittkopp, S.Wildman, D.Moshinsky. S.Potluri, H.Xi Predicting Kinase Selectivity Profiles Using Free-Wilson QSAR Analysis. J. Chem. Inf. Model. 2008 ePub available


University of Michigan, Ann Arbor, MI, 1997 - 2001

I worked in the lab of Dr. Gordon Crippen, first on the development of a program to accurately predict physicochemical parameters (logP, MR) based on atomic contributions. This involved a complete re-definition of the atom classification rules in SMARTS and identification of appropriate training data sets for logP and MR. A total of 68 atom types were defined for both properties. The logP contributions were determined by fitting a set of 9920 compounds from the logP STAR database (Pomona, 1998) and the MR contributions were fit to a set of 3412 compounds from the CRC Handbook. Both properties are prodicted accurately for most organic molecules.

Scott A. Wildman and Gordon M. Crippen Prediction of Physicochemical Properties by Atomic Contributions J. Chem. Inf. Comput. Sci. 1999, 39, 868-873.

Scott A. Wildman and Gordon M. Crippen Evaluation of Ligand Overlap by Atomic Parameters J. Chem. Inf. Comput. Sci. 2001, 41, 446-450.

This work progressed into the development of a method for 3D QSAR using the atomic property values and atom pair distances. The algorithm allows for smooth variation of the level of resolution and treats biological data with error bars rather than as specific values. The analysis is pretty neat, and if you're interested encourage you to take a look at the second and third references here. The last paper here describes a novel method for the description of molecular chirality, based on atomic property values.

Gordon M. Crippen and Scott A. Wildman Quantitative Structure-Activity Relationships (QSAR): A Review of 3D QSAR in Combinatorial Library Design and Evaluation: Principles, Software Tools and Applications Ghose, A. K.; Viswanadhan, V. N. Eds., Dekker, New York, 2001.

Scott A. Wildman and Gordon M. Crippen Three-Dimensional Molecular Descriptors and a Novel QSAR Method J. Mol. Graphics Modell. 2002 21, 161-170.

Scott A. Wildman and Gordon M. Crippen Validation of DAPPER for 3D QSAR: Conformational Search and Chirality Metric J. Chem. Inf. Comput. Sci. 2003, 43, 629-636.


Clarkson University, Potsdam, NY, 1994 - 1996

I worked in the computational chemistry laboratory of Dr. Phillip A. Christiansen studying the electronic structure of small molecules and relativistic effects on heavy main-group elements. We were particularly concerned with the generation of accurate relativistic effective potentials (REPs) for sixth-row main group elements.

Using REPs is an accurate, ab-initio, way to calculate properties (bond length, dissociation energy, vibrational states, etc.) for molecules containing heavy elements without explicitly representing all the electrons of the heavy atom(s). While REPs for sixth-row elements have been derived by several groups using several different methods, our goal was to eliminate errors present in previous REPs and accurately predict properties from all-electron calculations.

More information is available for:
P.A. Christiansen's research at: The REP Database and through Clarkson Chemistry.

Scott A. Wildman, Gino A. DiLabio, and Phillip A. Christiansen, Accurate Relativistic Effective Potentials for Sixth-row Main Group Elements J. Chem. Phys. 1997, 107, 9975-9979.

This material was presented at the 53rd Ohio State University International Symposium on Molecular Spectroscopy, June 1998

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