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 relationships (QSARs), which play an important role during a drug design process. Several approaches for the conversion of a QSAR problem to suitable learning tasks for the FA are presented. Finally the FA is applied to a well-known QSAR benchmark, viz. the inhibition of E. coli dihydrofolate reductase by triazines. The achieved results are compared with results of other machine learning approaches on the same QSAR benchmark, and prove that the FA is signiicantly better. activity relationships, inhibition of E. coli dihydrofolate reductase by triazines.