Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform

Abstract

Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.

A Deep-Learning Proteomic-Scale Approach for Drug Design

“…While computational experiments are an important first step and indicate promising, prospective preclinical validation of the pipeline, its designs will require medicinal chemistry synthesis, binding studies, and disease models assays at multiple scales, which we are currently undertaking. Regardless, our combined work to date [18][19][20][21][22][23][24][25][26][27][28][29], including this study, indicates that novel high-throughput methods for rapidly identifying relationships between compounds, proteins, pathways, and cells are highly desirable for holistic drug discovery. As CANDO is agnostic to the specific methods used for any of its protocols, should such data become available, the RCVAE design pipeline described here would be well poised to take advantage of them for maximum drug discovery efficiency.…”

“…A fundamental tenet of CANDO is that evaluating all the possible interactions between a human-ingested drug/compound and the macromolecules and systems it encounters on a proteomic/interactomic scale is necessary to determine its safety and efficacy for a given indication [18][19][20][21][22][23][24][25][26][27][28][29]. The RCVAE design pipeline represents a significant step forward in early drug discovery as it allows for the virtually unlimited generation of novel putative drug candidates to treat any indication/disease by combating it on a proteomic scale.…”

“…We are currently exploring other scoring protocols within the CANDO platform for conditional generation to overcome the limitations of any specific interaction calculation method. For example, the interaction scoring protocol used in this work has been shown to have great utility in the context of evaluating proteomic behavioral similarity based on benchmarking performance [18][19][20][21][22][23][24][25][26][27][28][29]. However, information on agonism/antagonism, and downstream functional activity upon binding may be obtained via design pipelines that utilize gene and protein expression data, which we are incorporating into CANDO.…”

“…Computational methods are efficient, accurate, holistic (i.e., take into account the entire interaction space of chemical entities), and have breadth in terms of chemical space exploration necessary to overcome the limitations of traditional approaches [2,6,12,13,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. To expand compound libraries utilized in screening, combinatorial chemistry and machine-learning design pipelines have been developed to generate libraries of compounds likely to bind to a given target [35][36][37].…”

“…We developed the Computational Analysis of Novel Drug Repurposing Opportunities (CANDO) platform for shotgun multitarget drug discovery, repurposing, and design to overcome the aforementioned limitations of traditional single-target approaches [18][19][20][21][22][23][24][25][26][27][28][29]. The platform screens and ranks drugs/compounds for every disease/indication (and adverse event) through the large-scale modeling and analytics of the interactions between comprehensive libraries of drugs/compounds and protein structures.…”