Quantitative prediction of selectivity between the A₁ and A_2A adenosine receptors.

ABSTRACT: The development of drugs is often hampered due to off-target interactions leading to adverse effects. Therefore, computational methods to assess the selectivity of ligands are of high interest. Currently, selectivity is often deduced from bioactivity predictions of a ligand for multiple targets (individual machine learning models). Here we show that modeling selectivity directly, by using the affinity difference between two drug targets as output value, leads to more accurate selectivity predictions. We test multiple approaches on a dataset consisting of ligands for the A₁ and A_2A adenosine receptors (among others classification, regression, and we define different selectivity classes). Finally, we present a regression model that predicts selectivity between these two

SUBMITTER: Burggraaff L 

PROVIDER: S-EPMC7222572 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature