Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines

Relating Chemical Structure to Activity: An Application of the Neural Folding Architecture

This paper is based on the neural folding architecture (FA). The FA is a recurrent neural network architecture which is especially suited for adaptive structure processing, i.e. learning approximations of mappings from symbolic term structures to IR n. The main objective of this paper is to demonstrate that the FA can be successfully applied to approximate quantitative structure activity relati...

Quantitative structure-activity relationships by evolved neural networks for the inhibition of dihydrofolate reductase by pyrimidines.

Evolutionary computation provides a useful method for training neural networks in the face of multiple local optima. This paper begins with a description of methods for quantitative structure activity relationships (QSAR). An overview of artificial neural networks for pattern recognition problems such as QSAR is presented and extended with the description of how evolutionary computation can be ...

Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.

The machine learning program GOLEM from the field of inductive logic programming was applied to the drug design problem of modeling structure-activity relationships. The training data for the program were 44 trimethoprim analogues and their observed inhibition of Escherichia coli dihydrofolate reductase. A further 11 compounds were used as unseen test data. GOLEM obtained rules that were statis...

Abstract Linear and nonlinear quantitative structure-activity relationship (QSAR) models and docking score functions were developed for dihydrofolate reductase (DHFR) inhibition by cycloguanil derivatives using small molecule descriptors derived from MOE and in silico docking

Linear and nonlinear quantitative structure-activity relationship (QSAR) models and docking score functions were developed for dihydrofolate reductase (DHFR) inhibition by cycloguanil derivatives using small molecule descriptors derived from MOE and in silico docking energies. The best QSAR models and docking score functions were identified when using artificial neural networks optimized by evo...

Evolutionary Neural Networks in Quantitative Structure Activity Relationships of Dihydrofolate Reductase Inhibi - tors