On the String Kernel Pre-Image Problem with Applications in Drug Discovery

TL;DR

This paper introduces a new low-complexity upper bound for the string kernel pre-image problem, enabling efficient search algorithms with applications in drug discovery, particularly in identifying druggable peptides.

Contribution

It develops a novel upper bound for the string kernel pre-image problem and demonstrates its effectiveness in a branch and bound algorithm for drug discovery.

Findings

Effective in discovering druggable peptides

Provides a computationally efficient approach

Applicable to various string kernels

Abstract

The pre-image problem has to be solved during inference by most structured output predictors. For string kernels, this problem corresponds to finding the string associated to a given input. An algorithm capable of solving or finding good approximations to this problem would have many applications in computational biology and other fields. This work uses a recent result on combinatorial optimization of linear predictors based on string kernels to develop, for the pre-image, a low complexity upper bound valid for many string kernels. This upper bound is used with success in a branch and bound searching algorithm. Applications and results in the discovery of druggable peptides are presented and discussed.