Knowledge-Based Optimization of Molecular Geometries Using Crystal Structures

This paper describes a novel way to use the structural information contained in the Cambridge Structural Database (CSD) to drive geometry optimization of organic molecules. We describe how CSD structural information is transformed into objective functions for gradient-based optimization to provide good quality geometries for a large variety of organic molecules. Performance is assessed by minimizing different sets of organic molecules reporting RMSD movements for bond lengths, valence angles, torsion angles, and heavy atom positions.

[1]  Patrick McCabe,et al.  Kernel Density Estimation Applied to Bond Length, Bond Angle, and Torsion Angle Distributions , 2014, J. Chem. Inf. Model..

[2]  Sarah L Price,et al.  Analysis of the conformational profiles of fenamates shows route towards novel, higher accuracy, force-fields for pharmaceuticals. , 2015, Physical chemistry chemical physics : PCCP.

[3]  F. Allen.  The Cambridge Structural Database: a quarter of a million crystal structures and rising. , 2002, Acta crystallographica. Section B, Structural science.

[4]  Tamar Schlick,et al.  A recipe for evaluating and differentiating cos ϕ expressions , 1989 .

[5]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[6]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[7]  Robin Taylor,et al.  Comparison of conformer distributions in the crystalline state with conformational energies calculated by ab initio techniques , 1996, J. Comput. Aided Mol. Des..

[8]  Sarah L Price,et al.  Successful prediction of a model pharmaceutical in the fifth blind test of crystal structure prediction. , 2011, International journal of pharmaceutics.

[9]  J. Bernstein,et al.  Conformational polymorphism. , 2014, Chemical reviews.

[10]  Mauro Birattari,et al.  The irace Package: Iterated Race for Automatic Algorithm , 2011 .

[11]  Tjelvar S. G. Olsson,et al.  The good, the bad and the twisted: a survey of ligand geometry in protein crystal structures , 2012, Journal of Computer-Aided Molecular Design.

[12]  Gerhard Klebe,et al.  Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures , 1994, J. Chem. Inf. Comput. Sci..

[13]  A. Bondi.  van der Waals Volumes and Radii , 1964 .

[14]  Gerhard Klebe,et al.  DSX: A Knowledge-Based Scoring Function for the Assessment of Protein-Ligand Complexes , 2011, J. Chem. Inf. Model..

[15]  Robin Taylor,et al.  Intermolecular Nonbonded Contact Distances in Organic Crystal Structures: Comparison with Distances Expected from van der Waals Radii , 1996 .

[16]  W F van Gunsteren,et al.  A structure refinement method based on molecular dynamics in four spatial dimensions. , 1993, Journal of molecular biology.

[17]  Patrick McCabe,et al.  Knowledge-Based Libraries for Predicting the Geometric Preferences of Druglike Molecules , 2014, J. Chem. Inf. Model..

[18]  Simon J. A. Malham.  Introduction to applied mathematics Lecture notes , 2009 .

[19]  T. Halgren.  Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[20]  R. Cramer,et al.  Validation of the general purpose tripos 5.2 force field , 1989 .

[21]  Randy J. Read,et al.  A New Generation of Crystallographic Validation Tools for the Protein Data Bank , 2011, Structure.

[22]  A Godzik,et al.  Knowledge-based potentials for protein folding: what can we learn from known protein structures? , 1996, Structure.

[23]  Jie Luo,et al.  Retrieval of Crystallographically-Derived Molecular Geometry Information , 2004, J. Chem. Inf. Model..