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Neighborhood Behavior: A Useful Concept for Validation of “Molecular Diversity” Descriptors

David E. Patterson,* Richard D. Cramer, Allan M. Ferguson, Robert D. Clark, and Laurence E. Weinberger^†

Tripos, Inc., 1699 South Hanley Road, St. Louis, Missouri 63144, and 882 South Matlack Street, West Chester, Pennsylvania 19380

J. Med. Chem., 1996, 39 (16), pp 3049–3059

DOI: 10.1021/jm960290n

Publication Date (Web): August 2, 1996

To whom correspondence should be addressed.

†

West Chester, PA.

Abstract

When searching for new leads, testing molecules that are too “similar” is wasteful, but when investigating a lead, testing molecules that are “similar” to the lead is efficient. Two questions then arise. Which are the molecular descriptors that should be “similar”? How much “similarity” is enough? These questions are answered by demonstrating that, if a molecular descriptor is to be a valid and useful measure of “similarity” in drug discovery, a plot of differences in its values vs differences in biological activities for a set of related molecules will exhibit a characteristic trapezoidal distribution enhancement, revealing a “neighborhood behavior” for the descriptor. Applying this finding to 20 datasets allows 11 molecular diversity descriptors to be ranked by their validity for compound library design. In order of increasing frequency of usefulness, these are random numbers = log P = MR = strain energy < connectivity indices < 2D fingerprints (whole molecule) = atom pairs = autocorrelation indices < steric CoMFA fields = 2D fingerprints (side chain only) = H-bonding CoMFA fields.

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Published In Issue August 02, 1996
Received April 18, 1996

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Expedited Article

Neighborhood Behavior: A Useful Concept for Validation of “Molecular Diversity” Descriptors

Abstract

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History

Recommend & Share

Related Content

Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships

Development of QSAR Models To Predict and Interpret the Biological Activity of Artemisinin Analogues

Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors

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