Nonlinear Prediction of Quantitative Structure-Activity Relationships
نویسندگان
چکیده
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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ورودعنوان ژورنال:
- Journal of chemical information and computer sciences
دوره 44 5 شماره
صفحات -
تاریخ انتشار 2004