Polypharmacology: challenges and opportunities in drug discovery.
By A. Anighoro, Jürgen Bajorath, Giulio Rastelli. Journal of Medicinal Chemistry, 2014
932 citations recorded by the source corpus.
At present, the legendary magic bullet, i.e., a drug with high potency and selectivity toward a specific biological target, shares the spotlight with an emerging and alternative polypharmacology approach. Polypharmacology suggests that more effective drugs can be developed by specifically modulating multiple targets. It is generally thought that complex diseases such as cancer and central nervous system diseases may require complex therapeutic approaches. In this respect, a drug that "hits" multiple sensitive nodes belonging to a network of interacting targets offers the potential for higher efficacy and may limit drawbacks generally arising from the use of a single-target drug or a combination of multiple drugs. In this review, we will compare advantages and disadvantages of multitarget versus combination therapies, discuss potential drug promiscuity arising from off-target effects, comment on drug repurposing, and introduce approaches to the computational design of multitarget drugs.
Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry
View Paper: https://www.semanticscholar.org/paper/c46e6173c40e7f4cfb64e88d3aba9607461d5621
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Citations in the corpus
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, background): “It is best suited for designing a promising therapeutic agent for more complex diseases such as cancer, neurodegenerative disease (NDDs), diabetes, heart failure, and many others [353–355].”