Discovering second-order sub-structure associations in drug molecules for side-effect prediction

Abstract

Possible drug side-effects (SEs) are usually verified by many years of repeated clinical trials. Despite the effort, some drugs are still expected to cause adverse reactions in some patients. To better predict drug SEs without having to go through the laborious processes of testing and re-testing, machine learning (ML) techniques are more and more used to uncovered patterns in drug data for such purpose. Most existing such techniques are black-box techniques. Since correlations between sub-structures involving multiple variables may exist, these techniques may not always work well. For ML techniques to be effective, they should be accurate, efficient and the patterns they discover should be interpretable. Towards these goals, we have developed a second-order association discovering (SOAD) algorithm for SE prediction. Given a set of drug data for training, the SOAD algorithm can discover SO associations between multiple drug sub-structures and multiple SEs in drug data for the purpose of predicting the SEs. SOAD performs its tasks by first making use of a residual measure to test the significance of occurrence of a chemical sub-structure within a drug and the SE of the drug. Once an association is established between a sub-structure and a SE, we test if two or more such sub-structures are significantly associated with a SE. Based on such second-order associations, we derive from them a set of “informative” SO patterns so that the SEs of new unseen drugs can be predicted based on the frequency of appearance of such patterns. To ensure interpretability of the SE discovery process, we make use of the Bayesian to predict if certain SO relationship in a drug may be related to a certain side-effect. Based on the experimental results, SOAD is found to be very promising.