Computational Approaches to Drug Repurposing and Pharmacology

By R. Hodos, Brian A. Kidd, S. Khader, B. Readhead, Joel T. Dudley. 2016

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing- finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we rationalize that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action.

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Citations in the corpus

Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, background): “Furthermore, integration of multiple channels of evidence, particularly diverse types of data, can overcome the limitations and weak performance inherent to data of a single domain ( Hodos et al., 2016 ).”
Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 protease against COVID-19 (2020, unknown): “Many drugs have multiple protein targets and many diseases share overlapping molecular pathways (Hodos et al., 2016).”
Utilizing graph machine learning within drug discovery and development (2021, background): “As such, there is a major interest in using in silico methods to screen and infer new treatment hypotheses [208].”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “ut which chemical compounds they bind [18]. Furthermore, the number of associated compounds for each gene is highly uneven [19], with many uncharacterized genes playing important roles in drug action [20]. Additionally, biomedical data are hierarchically organized and span molecules, pathways, cells”
Machine learning applications in drug development (2019, methodology): “Another way of solving the drug repurposing problem, as emphasized by [27], is to see it as a recommender system problem, where an agent should ‘‘recommend” best available options.”