Drug Repurposing and Artificial Intelligence: From Liaison to Marriage

Abstract Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

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Artificial Intelligence–Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development (Preprint)

10.2196/preprints.22453 ◽

2020 ◽

Author(s):

Enrico Santus ◽

Nicola Marino ◽

Davide Cirillo ◽

Emmanuele Chersoni ◽

Arnau Montagud ◽

...

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Health Care ◽

Research And Development ◽

Precision Medicine ◽

Patient Outcomes ◽

Medical Literature ◽

Drug Repurposing ◽

Public Health Systems ◽

Improve Patient

UNSTRUCTURED Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

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Reinforcing the Supply Chain of COVID-19 Therapeutics with Expert-Coded Retrosynthetic Software

10.26434/chemrxiv.12765410.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yingfu Lin ◽

Zirong Zhang ◽

babak mahjour ◽

di wang ◽

rui zhang ◽

...

Keyword(s):

Artificial Intelligence ◽

Supply Chain ◽

General Solution ◽

Supply Chains ◽

Raw Materials ◽

Drug Repurposing ◽

Hand Sanitizer ◽

Toilet Paper ◽

Global Demand

Supply chains become stressed when demand for essential products increases rapidly in times of crisis. This year, the scourge of coronavirus highlighted the fragility of diverse supply chains, affecting the world’s pipeline of hand sanitizer, 1 toilet paper,2 and pharmaceutical starting materials. 3 Many drug repurposing studies are now underway. 4 If a winning therapeutic emerges, it is unlikely that the existing inventory of the medicine, or even the chemical raw materials needed to synthesize it,5 will be available in the quantities required to satisfy global demand. We show the use of a retrosynthetic artificial intelligence (AI) 6-10 to navigate multiple parallel synthetic sequences, and arrive at plausible alternate reagent supply chains for twelve investigational COVID-19 therapeutics. In many instances, the AI utilizes C–H functionalization logic, 11-13 and we have experimentally validated several syntheses, including a route to the antiviral umifenovir that requires functionalization of six C–H bonds. This general solution to chemical supply chain reinforcement will be useful during global disruptions, such as during a pandemic.

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Artificial intelligence against COVID-19 Pandemic: A Comprehensive Insight

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666211004115208 ◽

2021 ◽

Vol 17 ◽

Author(s):

Azhar Equbal ◽

Sarfaraz Masood ◽

Iftekhar Equbal ◽

Shafi Ahmad ◽

Noor Zaman Khan ◽

...

Keyword(s):

Artificial Intelligence ◽

Modern Science ◽

Drug Repurposing ◽

Extensive Literature ◽

Imaging Data ◽

Trend Prediction ◽

Infective Disease ◽

Novel Coronavirus ◽

Insight Into

: COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infective disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives with emergency use authorisation vaccines are being done across many countries, however, their long term efficacy and side-effects study are yet to be done. The research community is analysing the situation by collecting the datasets from various sources. Healthcare professionals must thoroughly analyse the situation, devise preventive measures for this pandemic, and even develop possible drug combinations. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses on the application of state-of-the-art methods in this combat against COVID-19. The application of Artificial intelligence (AI), and AI-driven tools are emerging as effective tools, especially with X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions etc. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e. >95%, as reported in various studies. AI-driven tools are being used in COVID diagnostic, therapeutics, trend prediction, drug design and prevention to help fight against this pandemic. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in this battle against the COVID-19 pandemic. The extensive literature is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 Prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing.

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Multi-Layer Picture of Neurodegenerative Diseases: Lessons from the Use of Big Data through Artificial Intelligence

Journal of Personalized Medicine ◽

10.3390/jpm11040280 ◽

2021 ◽

Vol 11 (4) ◽

pp. 280

Author(s):

Andrea Termine ◽

Carlo Fabrizio ◽

Claudia Strafella ◽

Valerio Caputo ◽

Laura Petrosini ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Deep Learning ◽

Early Diagnosis ◽

Neurodegenerative Diseases ◽

Neurodegenerative Disorders ◽

Drug Repurposing ◽

Biomedical Data ◽

Parkinson’S Diseases ◽

Made In

In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases through the integration of all sources of biomedical data.

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A Systematic Review on the Contribution of Artificial Intelligence in the Development of Medicines for COVID-2019

Journal of Personalized Medicine ◽

10.3390/jpm11090926 ◽

2021 ◽

Vol 11 (9) ◽

pp. 926

Author(s):

Carla Pires

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Drug Repurposing ◽

Cochrane Library ◽

Angiotensin Converting Enzyme 2 ◽

Drug Databases ◽

Repurposed Drugs ◽

Meta Analyses ◽

New Treatments

Background: COVID-2019 pandemic lead to a raised interest on the development of new treatments through Artificial Intelligence (AI). Aim: to carry out a systematic review on the development of repurposed drugs against COVID-2019 through the application of AI. Methods: The Systematic Reviews and Meta-Analyses (PRISMA) checklist was applied. Keywords: [“Artificial intelligence” and (COVID or SARS) and (medicine or drug)]. Databases: PubMed®, DOAJ and SciELO. Cochrane Library was additionally screened to identify previous published reviews on the same topic. Results: From the 277 identified records [PubMed® (n = 157); DOAJ (n = 119) and SciELO (n = 1)], 27 studies were included. Among other, the selected studies on new treatments against COVID-2019 were classified, as follows: studies with in-vitro and/or clinical data; association of known drugs; and other studies related to repurposing of drugs. Conclusion: Diverse potentially repurposed drugs against COVID-2019 were identified. The repurposed drugs were mainly from antivirals, antibiotics, anticancer, anti-inflammatory, and Angiotensin-converting enzyme 2 (ACE2) groups, although diverse other pharmacologic groups were covered. AI was a suitable tool to quickly analyze large amounts of data or to estimate drug repurposing against COVID-2019.

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Artificial Intelligence–Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development

Journal of Medical Internet Research ◽

10.2196/22453 ◽

2021 ◽

Vol 23 (3) ◽

pp. e22453 ◽

Cited By ~ 1

Author(s):

Enrico Santus ◽

Nicola Marino ◽

Davide Cirillo ◽

Emmanuele Chersoni ◽

Arnau Montagud ◽

...

Keyword(s):

Public Health ◽

Artificial Intelligence ◽

Health Care ◽

Research And Development ◽

Precision Medicine ◽

Patient Outcomes ◽

Medical Literature ◽

Drug Repurposing ◽

Public Health Systems ◽

Improve Patient

Artificial intelligence (AI) technologies can play a key role in preventing, detecting, and monitoring epidemics. In this paper, we provide an overview of the recently published literature on the COVID-19 pandemic in four strategic areas: (1) triage, diagnosis, and risk prediction; (2) drug repurposing and development; (3) pharmacogenomics and vaccines; and (4) mining of the medical literature. We highlight how AI-powered health care can enable public health systems to efficiently handle future outbreaks and improve patient outcomes.

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Application of Artificial Intelligence in Drug Repurposing: A mini-review

Current Chinese Science ◽

10.2174/2210298101666210204162006 ◽

2021 ◽

Vol 01 ◽

Author(s):

Gurudeeban Selvaraj ◽

Satyavani Kaliamurthi ◽

Gilles H. Peslherbe ◽

Dong-Qing Wei

Keyword(s):

Artificial Intelligence ◽

Drug Repositioning ◽

Target Identification ◽

Drug Repurposing ◽

Search Term ◽

Biomedical Data ◽

Drug Candidates ◽

Drug Target Identification ◽

Speed Up ◽

Tools And Techniques

Background and aim: Advancement of extra-ordinary biomedical data (genomics, proteomics, metabolomics, drug libraries, and patient care data), evolution of super-computers, and continuous development of new algorithms that lead to a generous revolution in artificial intelligence (AI). Currently, many biotech and pharmaceutical companies made reasonable investments in and have co-operation with AI companies and increasing the chance of better healthcare tools development, includes biomarker and drug target identification, designing a new class of drugs and drug repurposing. Thus, the study is intended to project the pros and cons of AI in the application of drug repositioning. Methods: Using the search term “AI” and “drug repurposing” the relevant literatures retrieved and reviewed from different sources includes PubMed, Google Scholar, and Scopus. Results: Drug discovery is a lengthy process, however, leveraging the AI approaches in drug repurposing via quick virtual screening may enhance and speed-up the identification of potential drug candidates against communicable and non-communicable diseases. Therefore, in this mini-review, we have discussed different algorithms, tools and techniques, advantages, limitations on predicting the target in repurposing a drug. Conclusions: AI technology in drug repurposing with the association of pharmacology can efficiently identify drug candidates against pandemic diseases.

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Molecular Docking: Shifting Paradigms in Drug Discovery

International Journal of Molecular Sciences ◽

10.3390/ijms20184331 ◽

2019 ◽

Vol 20 (18) ◽

pp. 4331 ◽

Cited By ~ 33

Author(s):

Luca Pinzi ◽

Giulio Rastelli

Keyword(s):

Artificial Intelligence ◽

Molecular Docking ◽

Drug Discovery ◽

Chemical Structure ◽

Molecular Level ◽

A Priori ◽

Drug Repurposing ◽

Large Molecules ◽

Structure Activity ◽

Target Fishing

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

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