Nonlinear Prediction of Quantitative Structure−Activity Relationships | Zendy

Peter Tiňo | Zendy; Ian T. Nabney | Zendy; Bruce S. Williams | Zendy; Jens Lösel | Zendy; Yi Sun | Zendy

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Nonlinear Prediction of Quantitative Structure−Activity Relationships

Author(s) -

Peter Tiňo,

Ian T. Nabney,

Bruce S. Williams,

Jens Lösel,

Yi Sun

Publication year - 2004

Publication title -

journal of chemical information and computer sciences

Language(s) - English

Resource type - Journals

eISSN - 1520-5142

pISSN - 0095-2338

DOI - 10.1021/ci034255i

Subject(s) - nonlinear system , biological system , computer science , mathematics , algorithm , physics , biology , quantum mechanics

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.

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