Fig. 4: Analysis of target affinity among the top-scoring drugs.
From: Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

a Overview of target affinity spectrum (TAS) score computation from raw drug binding data. Three types of drug binding data were sourced from ChEMBL and from the internal Laboratory Systems of Pharmacology dataset that have not yet been incorporated into ChEMBL. Empirically derived thresholds for the different data types were used to assign TAS scores to each drug–target pair. Multiple measurements for the same drug–target combination were aggregated along the first quartile to define the final TAS value. b Binding affinity of compounds in the ranked list to every member of the Janus Kinase family. The compounds are sorted in increasing order by the harmonic mean p-value (as defined in Fig. 3) along the x-axis. The top heatmap shows the binding affinity of each compound to the selected targets, explicitly naming the FDA-approved drugs. Colored and gray tiles denote confirmed binders and non-binders, respectively; missing entries correspond to unknown affinity values. The combined affinity is defined as the strongest binding (lowest TAS score) among all four JAK targets. The bottom plot shows the breakdown of the combined affinity values by TAS-specific empirical cumulative distribution functions (ECDFs). Each line shows ECDFs for all drugs that bind the corresponding target with a TAS score of 1 (dark orange), 2 (orange), or 3 (light orange). c Top targets whose binding affinity correlates most strongly with the compound ranking. The ECDFs of confirmed non-binders (TAS = 10) are shown as gray dashed lines for reference. Area under ECDF can be interpreted as a summary statistic that captures the position of drugs binding to that target with the corresponding affinity in the ranked list. Correlation between the drug ranking and TAS values was computed using the one-sided Kendall’s Tau test, with the associated unadjusted p-value displayed in the bottom right corner of each plot.