Pairwise Auxiliary Ranking Task for Bioactivity Modelling

ChemRxiv

Abstract

Machine-learning models that predict bioactivity of novel compounds are dependent on the quality, quantity, and relatedness of their training data. For regression modelling, large amounts of publicly available lower-quality data need to be disregarded due to significant noise or incompatible endpoints. In this paper, we implement a pairwise auxiliary ranking task in a message-passing neural network for proteochemometrics regression bioactivity modelling. The auxiliary training task compares a relative ranking of the affinity of different compound pairs and runs in parallel to the standard regression training. This simple modification allows for the extension of training data with percentage-inhibition data points, increasing the chemical space available to regression models. We benchmark models on data sampled from standard sources, as well as on data related to the extended training data to characterize the influence of these modifications. Significant performance increases can be achieved on related data without sacrificing predictive power on standard tests. The pairwise auxiliary ranking task is flexible in the types of endpoints it can support and the regression problems it can address.

Authors 3

1. Marc Boef Aachen

   Leiden University

   Affiliation as printed

   Leiden University

   Oncode Accelerator

2. Olivier J. M. Béquignon Aachen

   Leiden University

   Affiliation as printed

   Leiden University

   Oncode Accelerator

3. Gerard J. P. van Westen Aachen

   Leiden University

   Affiliation as printed

   Leiden University

   Oncode Accelerator