Fingerprinting CANDO: Increased Accuracy with Structure- and Ligand-Based Shotgun Drug Repurposing

We
have upgraded our Computational Analysis of Novel Drug Opportunities
(CANDO) platform for shotgun drug repurposing by including ligand-based,
data fusion, and decision tree pipelines. The goal of shotgun drug
repurposing is to screen and rank every existing human use drug or
compound for every disease/indication. The first version of CANDO
implemented a structure-based pipeline that modeled interactions between
compounds and proteins on a large scale, generating compound–proteome
interaction signatures used to infer the similarity of drug behavior;
the new pipelines accomplish this by incorporating molecular fingerprints
and the Tanimoto coefficient. We obtain improved benchmarking performance
with the new pipelines across all three evaluation metrics used: average
indication accuracy, pairwise accuracy, and coverage. The best performing
pipeline achieves an average indication accuracy of 19.0% at the top10
cutoff, compared to 11.7% for v1, and 2.2% for a random control. Our
results demonstrate that the CANDO drug recovery accuracy is substantially
improved by integrating multiple pipelines, thereby enhancing our
ability to generate putative therapeutic repurposing candidates, and
increasing drug discovery efficiency.