Nonlinear Prediction of Quantitative Structure-Activity Relationships

Predicting the log of the partition coefficient P is a long-standing benchmark problem in Quantitative Structure-Activity Relationships (QSAR). In this paper we show that a relatively simple molecular representation (using 14 variables) can be combined with leading edge machine learning algorithms to predict logP on new compounds more accurately than existing benchmark algorithms which use complex molecular representations.

[1]  A. Leo,et al.  Substituent constants for correlation analysis in chemistry and biology , 1979 .

[2]  Gene H. Golub,et al.  Matrix computations , 1983 .

[3]  A. Ghose,et al.  Atomic Physicochemical Parameters for Three‐Dimensional Structure‐Directed Quantitative Structure‐Activity Relationships I. Partition Coefficients as a Measure of Hydrophobicity , 1986 .

[4]  Arup K. Ghose,et al.  Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics , 1989, J. Chem. Inf. Comput. Sci..

[5]  H. White,et al.  Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions , 1989, International 1989 Joint Conference on Neural Networks.

[6]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[7]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[8]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[9]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[10]  S. Hirono,et al.  Simple Method of Calculating Octanol/Water Partition Coefficient. , 1992 .

[11]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[12]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[13]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[14]  Luís Torgo,et al.  Functional Models for Regression Tree Leaves , 1997, ICML.

[15]  J. Sangster.  Octanol-Water Partition Coefficients: Fundamentals and Physical Chemistry , 1997 .

[16]  Christopher K. I. Williams.  Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond , 1999, Learning in Graphical Models.

[17]  Han van de Waterbeemd,et al.  Substructure and whole molecule approaches for calculating log P , 2001, J. Comput. Aided Mol. Des..

[18]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

[19]  Lehel Csató,et al.  Sparse On-Line Gaussian Processes , 2002, Neural Computation.

[20]  Peter Tiño,et al.  Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way , 2002, IEEE Trans. Pattern Anal. Mach. Intell..