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Efficient Calculation of Molecular Properties from Simulation Using Kernel Molecular Dynamics

Supporting Info

W. Michael Brown*^†, Ariella Sasson^‡, Donald R. Bellew^§, Lucy A. Hunsaker, Shawn Martin^†, Andrei Leitao, Lorraine M. Deck^§, David L. Vander Jagt and Tudor I. Oprea

Computational Biology, Sandia National Laboratories, P.O. Box 5800, M/S 1316, Albuquerque, New Mexico 87185-1316, Department of Computational Biology and Molecular Biophysics, Rutgers State University of New Jersey, Piscataway, New Jersey 08854, and Department of Chemistry and Chemical Biology and Department of Biochemistry and Molecular Biology, University of New Mexico, Albuquerque, New Mexico 87131

J. Chem. Inf. Model., 2008, 48 (8), pp 1626–1637

DOI: 10.1021/ci8001233

Publication Date (Web): August 2, 2008

* Corresponding author phone: (505)284-8938; fax: (505)845-7442; e-mail: wmbrown@sandia.gov.

†

Sandia National Laboratories.

‡

Rutgers State University of New Jersey.

Department of Chemistry and Chemical Biology, University of New Mexico.

Department of Biochemistry and Molecular Biology, University of New Mexico.

Abstract

Understanding the relationship between chemical structure and function is a ubiquitous problem within the fields of chemistry and biology. Simulation approaches attack the problem utilizing physics to understand a given process at the particle level. Unfortunately, these approaches are often too expensive for many problems of interest. Informatics approaches attack the problem with empirical analysis of descriptions of chemical structure. The issue in these methods is how to describe molecules in a manner that facilitates accurate and general calculation of molecular properties. Here, we present a novel approach that utilizes aspects of simulation and informatics in order to formulate structure−property relationships. We show how supervised learning can be utilized to overcome the sampling problem in simulation approaches. Likewise, we show how learning can be achieved based on molecular descriptions that are rooted in the physics of dynamic intermolecular forces. We apply the approach to three problems including the analysis of corticosteroid binding globulin ligand binding affinity, identification of formylpeptide receptor ligands, and identification of resveratrol analogues capable of inhibiting activation of transcription factor nuclear factor kappaB.

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