Chapter 21
Novel Structure—Activity Insights from Neural Network Models Tariq A. Andrea
Downloaded by TUFTS UNIV on June 12, 2018 | https://pubs.acs.org Publication Date: May 5, 1995 | doi: 10.1021/bk-1995-0606.ch021
Stine-Haskell Research Center, DuPont Agricultural Products P.O. Box 30, Newark, DE 19714
In regression QSAR, ligand/protein binding is a linear or parabolic function of ligands' physico-chemical properties. Due to the absence of higher order and cross-product terms, dependence of binding on one property is invariant to others. By comparison, neural networks are capable of delineating highly non-linear features. Stepwise regression of benzene sulfonylureas' binding to acetolactate synthases enzyme indicates that, properties of the ortho substituent R have the following 2
order of significance: MR > π >F. Affinity depends parabolically on MR (MR =14.05) and increases linearly with π and F. The constant curvature (-0.0158) of the MR parabola indicates that the binding pocket tolerates R 's with 4-7 heavy atoms and that this tolerance is opt
2
invariant to π and F. This suggests an enzyme pocket with fixed size. Neural networks analysis finds the same order of significance of R properties. While in this model MR dependence is not mathematically parabolic, it has a "parabolic or Guassian-like" shape. Like regression QSAR, the neural model indicates that optimal MR and tolerance to size variation depends on π and F. It suggests a binding pocket which accommodates larger hydrophobic and electron withdrawing substituents than hydrophilic and electron donating ones. It also indicates higher tolerance to size variations in hydrophobic and electron withdrawing substituents than in hydrophilic and electron donating ones. These are consistent with x-ray crystallographic findings that even structurally related ligands can bind differently to the same protein. 2
In the original formulation of the field of quantitative structure-activity relationships (QSAR), introduced in the early 1960s by Hansch and co-workers, biological activities of chemical compounds are linear or parabolic polynomial functions of their physico-chemical properties (π, MR, σ...). Activity = a + b J l + c π + d M R + e M R + f σ + ... + ... 2
2
(1)
0097-6156/95/0606-0282$12.00/0 © 1995 American Chemical Society Hansch and Fujita; Classical and Three-Dimensional QSAR in Agrochemistry ACS Symposium Series; American Chemical Society: Washington, DC, 1995.
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Higher order non-linear and cross products terms are not used in practice. Multiple linear regression is used to calculate the a, b, ... coefficients . Back Propagation neural networks were introduced in QSAR a few years ago (1,2,3). The initial thrust was to compare their predictions with those of regression QSAR. These publications alluded to, albeit did not expound, the non-linear modeling capabilities of neural networks. The objective of this publication is to demonstrate the ability of neural networks to delineate non-linear relationships between biological activity and physico-chemical properties and to explicate the novel insights they provide. This theme is developed using data from the inhibition of acetolactate synthase (ALS) enzyme by herbicidal sulfonylureas^).
Downloaded by TUFTS UNIV on June 12, 2018 | https://pubs.acs.org Publication Date: May 5, 1995 | doi: 10.1021/bk-1995-0606.ch021
Data and Model Development Table 1 (5) shows structures and A L S enzyme inhibitory activities of benzenesulfonylureas used in calculating regression and neural QSAR's. I50 is the molar concentration for 50% enzyme inhibition. Log (I/I50) was modeled as a function of π (Hansch's hydrophobicity parameter), M R (molar refractivity), and Swain-Lupton's electronic parameters
