Artificial intelligence, drug repurposing and peer review

Levin, Jeremy; Oprea, Tudor I.; Davidovich, Sagie; Clozel, Thomas; Overington, John P.; Vanhaelen, Quentin; Cantor, Charles R.; Bischof, Evelyne; Zhavoronkov, Alex

doi:10.1038/s41587-020-0686-x

Cited by 62 publications

(33 citation statements)

References 33 publications

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“…As for drug repurposing and clinical trials, AI should be an extension to human workflows, done in multidisciplinary teams so to guarantee that ML/DL strategies are tailored to specific scenarios. Furthermore, the complexities of designing AI solutions for molecular generation must be developed and strengthened, as well as best practice for data exchange (Levin et al 2020 ). As for drug repurposing and clinical trials, while there are overlaps between computationally predicted drug repurposing and clinical researches, despite the promising potential of AI applications on this topic, we found no concrete evidence that clinical trials were performed based on computational guidance.…”

Section: Discussion: Where We Are and What Is Nextmentioning

confidence: 99%

“…Natural Language Processing methodologies can be used to quickly analyze the scientific results produced in literature (Doanvo et al 2020;Kricka et al 2020;Levin et al 2020). In particular Doanvo et al (2020) used NLP to lemmatize words and preprocess text, and achieved dimensionality reduction with SVD, and topic modeling with its LDA.…”

Section: After the First Peak Of The Pandemicmentioning

confidence: 99%

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The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

et al. 2021

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The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

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Section: Discussion: Where We Are and What Is Nextmentioning

confidence: 99%

Section: After the First Peak Of The Pandemicmentioning

confidence: 99%

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

et al. 2021

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“…Molecular docking is an in-silico approach for the designing of drugs on the basis of their structures as it has the ability to predict the conformational properties of small molecules or ligands attached to drug or appropriate target binding site. But the drawback lies in the fact that it is unable to resolve the molecular 3D structure of many of the drug candidates required for molecular docking or simulation [44]. Moreover, another thing the accuracy of the methods gets decreasing when there are small or insufficient number of ligands available for a drug molecule.…”

Section: ) Algorithms For Molecular Docking and Simulationmentioning

confidence: 99%

Role of Artificial Intelligence and Machine Learning in Bioinformatics: Drug Discovery and Drug Repurposing

Quazi¹

2021

Preprint

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Artificial intelligence AI or machine learning has proven to be a potential activity in the health and biomedical sciences. Previous research it has found that AI can learn new data and transform it into the useful knowledge. In the field of pharmacology, the aim is to design more efficient and novel vaccines using this method which are also cost effective. The underlying fact is to predict the molecular mechanism and structure for increased likelihood of developing new drugs. Clinical, electronic and high resolution imaging datasets can be used as inputs to aid the drug development niche. Moreover, the use of comprehensive target activity has been performed for repurposing a drug molecule by extending target profiles of drugs which also include off targets with therapeutic potential providing a new indication.

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“…Drug repurposing strategies provide an attractive and effective approach based on available drug characteristics -drug-related pharmacology and toxicologyfor rapid therapeutic selection 8 . If we could, with higher probability, identify and pre-select the most promising hypothesis-based candidates using in silico systems biology tools, prior to costly and laborious in vitro and in vivo experiments and ensuing clinical trials, we could significantly improve disease-specific drug development 9 .…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19

Maria

Rapicavoli

Alaimo

et al. 2021

Preprint

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The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with very few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which ̶ by leveraging available transcriptomic and proteomic databases ̶ allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both > 96%) the viral effects on cellular host-immune response, resulting in a specific cellular SARS-CoV-2 signature and ii) utilize this specific signature to narrow down promising repurposable therapeutic strategies. Powered by this tool, coupled with domain expertise, we have identified several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential new druggable targets in COVID-19 pathogenesis.

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Artificial intelligence, drug repurposing and peer review

Cited by 62 publications

References 33 publications

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

Role of Artificial Intelligence and Machine Learning in Bioinformatics: Drug Discovery and Drug Repurposing

Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19

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