scholarly journals Concept of Artificial Intelligence in Discovering and Re-Purposing of Drugs

2021 ◽  
Author(s):  
Sushant Bajpai ◽  
Nehil Shreyash ◽  
Muskan Sonker ◽  
Vishwas Gupta ◽  
Saurabh Kr Tiwary ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
Therapeutic Effect ◽  
Drug Combination ◽  
Drug Repurposing ◽  
Treatment Method ◽  
Traditional Methods ◽  
Time Consumption ◽  
The Past ◽  
Clinical Level

Artificial Intellignece (AI) is a platform lending immense assistance in discovering and developing drugs and thus, various such approaches have been developed with the intent of simplifying and improving biomedical operations such as drug repurposing and drug discovery. In the past decade, AI-based investigation of nanomedicines, as well as non-nanomedicines has reached the clinical level. In semblance with the traditional methods of therapy, nanomedicine therapy is employed at limited doses. The study of a variety of drugs resulted in the conclusion that the effect of each drug is variable for every patient and, evaluating that perfect drug combination manually is a time-consuming as well as an inefficient treatment method. Therefore, the use of AI simplifies and reduces the time consumption in determining the perfect customized drug combination for nano-therapy. The area with the most potential for meeting this reality is to optimize the drug and dosage parameters. It is a universally known fact that cancer is dangerous and unique because of the exacting challenges it poses during treatment and, to achieve a better treatment, the therapeutic effect on each patient must be delineated even if the volume of data generated is massive. The article aims at analyzing the AI technologies that help yield results much quicker, make the analyses simple, and efficient.

scholarly journals Molecular Docking: Shifting Paradigms in Drug Discovery

2019 ◽  
Vol 20 (18) ◽  
pp. 4331 ◽  
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.

scholarly journals Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach

2021 ◽  
Author(s):  
Ruby Srivastava
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Repurposing ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Modern Drug ◽  
Short Period

Computational methods play a key role in the design of therapeutically important molecules for modern drug development. With these “in silico” approaches, machines are learning and offering solutions to some of the most complex drug related problems and has well positioned them as a next frontier for potential breakthrough in drug discovery. Machine learning (ML) methods are used to predict compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties to evaluate the drugs and their various applications. Modern artificial intelligence (AI) has the capacity to significantly enhance the role of computational methodology in drug discovery. Use of AI in drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials will certainly reduce the human workload as well as achieving targets in a short period of time. This chapter elaborates the crosstalk between the machine learning techniques, computational tools and the future of AI in the pharmaceutical industry.

scholarly journals COV-DrugX Pipeline for Covid-19 Drug Repurposing

2021 ◽  
Author(s):  
Robin Sinha ◽  
Preeti P
Keyword(s):  
Artificial Intelligence ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repurposing ◽  
Therapeutic Agents ◽  
The Novel ◽  
Novel Coronavirus ◽  
The Cost

The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has killed over 5 million people to date. Despite the introduction of population-wide vaccination drives, countries such as Austria and Germany are witnessing the re-emergence of infections and deaths. Scientists, administrators and clinicians are scrambling to find solutions that include vaccines, and active therapeutic agents. So, there is an urgent requirement for new and effective medications that can treat the disease caused by SARS-CoV-2. Artificial intelligence (AI) enabled drug repurposing, has the potential to shorten the time and reduce the cost compared to de novo drug discovery.

Quantitative Assessment of Expert's Lifting-Thrusting Manipulation in Acupuncture Based on Haptic Device

2013 ◽  
Vol 397-400 ◽  
pp. 2507-2510
Author(s):  
Peng Miao ◽  
Qi Zhang ◽  
Yuan Yuan Ji ◽  
Jun Jiang ◽  
Hai Dong Guo ◽  
...  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Therapeutic Effect ◽  
High Precision ◽  
Quantitative Assessment ◽  
Treatment Method ◽  
Haptic Device ◽  
Complementary Treatment ◽  
Traditional Methods ◽  
Quantitative Monitoring

Acupuncture is a complementary treatment method, while its therapeutic effect is tightly related to physicians manipulation skill. Lifting and thrusting manipulation is an important acupuncture procedure which could enhance the outcome, e.g. the arrival of Qi. Traditional methods cannot quantitatively measure acupuncture manipulations which make difficult in teaching and studying. In this paper, we proposed a high precision and quantitative monitoring system based on commercial haptic device. Our system can obtain quantitative position, velocity and force information and visualize the lifting and thrusting manipulation in real time. As a simple but accurate recording solution, it would be useful in accumulation and investigation of experts' experiences.

Sitagliptin: a potential drug for the treatment of SARS-CoV-2?

2020 ◽  
Author(s):  
Sanaa Bardaweel
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Antimalarial Drug ◽  
Drug Repurposing ◽  
Spike Protein ◽  
Diabetic Patients ◽  
Potential Drug ◽  
Chemokine Production ◽  
Drug Discovery And Development ◽  
Sequence Identity

Recently, an outbreak of fatal coronavirus, SARS-CoV-2, has emerged from China and is rapidly spreading worldwide. As the coronavirus pandemic rages, drug discovery and development become even more challenging. Drug repurposing of the antimalarial drug chloroquine and its hydroxylated form had demonstrated apparent effectiveness in the treatment of COVID-19 associated pneumonia in clinical trials. SARS-CoV-2 spike protein shares 31.9% sequence identity with the spike protein presents in the Middle East Respiratory Syndrome Corona Virus (MERS-CoV), which infects cells through the interaction of its spike protein with the DPP4 receptor found on macrophages. Sitagliptin, a DPP4 inhibitor, that is known for its antidiabetic, immunoregulatory, anti-inflammatory, and beneficial cardiometabolic effects has been shown to reverse macrophage responses in MERS-CoV infection and reduce CXCL10 chemokine production in AIDS patients. We suggest that Sitagliptin may be beneficial alternative for the treatment of COVID-19 disease especially in diabetic patients and patients with preexisting cardiovascular conditions who are already at higher risk of COVID-19 infection.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Alzheimer’s Drug Discovery Maze: A Snap View of the Past Decade’s Diverse Pharmacological Targets for the Disorder

2017 ◽  
Vol 17 (3) ◽  
pp. 305-318 ◽  
Author(s):  
Donald Sikazwe ◽  
Raghunandan Yendapally ◽  
Sushma Ramsinghani ◽  
Malik Khan
Keyword(s):  
Drug Discovery ◽  
The Past ◽  
Pharmacological Targets

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

Sign in / Sign up

Export Citation Format

Share Document