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

2021 ◽  
Author(s):  
Sameer Quazi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Therapeutic Potential ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Biomedical Sciences ◽  
Useful Knowledge ◽  
Resolution Imaging ◽  
Potential Activity

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.

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.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals Repurposing existing drugs for new uses: a cohort study of the frequency of FDA-granted new indication exclusivities since 1997

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Babak Sahragardjoonegani ◽  
Reed F. Beall ◽  
Aaron S. Kesselheim ◽  
Aidan Hollis
Keyword(s):  
Therapeutic Potential ◽  
Drug Repurposing ◽  
Status Quo ◽  
New Drugs ◽  
Generic Entry ◽  
Small Molecule Drugs ◽  
Clinical Investigations ◽  
Fda Approval ◽  
Limited Duration ◽  
Observation Period

Abstract Background Drug repurposing (i.e., finding novel uses for existing drugs) is essential for maximizing medicines’ therapeutic utility, but obtaining regulatory approval for new indications is costly. Policymakers have therefore created temporary indication-specific market exclusivities to incentivize drug innovators to run new clinical investigations. The effectiveness of these exclusivities is poorly understood. Objective To determine whether generic entry impacts the probability of new indication additions. Methods For a cohort of all new small-molecule drugs approved by the FDA between July 1997 and May 2020, we tracked new indications added for the subset of drugs that experienced generic entry during the observation period and then analyzed how the probability of a new indication changed with the number of years since/to generic entry. Results Of the 197 new drugs that subsequently experienced generic entry, only 64 (32%) had at least one new indication added. The probability of a new indication addition peaked above 4% between 7 and 8 years prior to generic entry and then to dropped to near zero 15 years after FDA approval. We show that the limited duration of exclusivity reduces the number of secondary indications significantly. Conclusion Status quo for most drug innovators is creating novel one-indication products. Despite indication-specific exclusivities, the imminence of generic entry still has a detectable impact on reducing the chances of new indication additions. There is much room for improvement when it comes to incentivizing clinical investigations for new uses and unlocking existing medicines’ full therapeutic potential.

Clinical Needs as a Starting Point for Different Strategies in Computational Drug Development

Drug Research ◽  
2018 ◽  
Vol 69 (08) ◽  
pp. 458-466 ◽  
Author(s):  
César Portela
Keyword(s):  
Drug Development ◽  
Translational Research ◽  
Computational Chemistry ◽  
Therapeutic Potential ◽  
Cost Effective ◽  
New Drugs ◽  
Medical Practitioners ◽  
Pathophysiological Mechanisms ◽  
Starting Point ◽  
The Way

AbstractTraditionally, the first step in the development of drugs is the definition of the target, by choice of a biological structure involved in a disease or by recognition of a molecule with some degree of a biological activity that presents itself as druggable and endowed with therapeutic potential. The complexity of the pathophysiological mechanisms of disease and of the structures of the molecules involved creates several challenges in this drug discovery process. These difficulties also come from independent operation of the different parts involved in drug development, with little interaction between clinical practitioners, academic institutions and large pharmaceutical companies. Research in this area is purpose specific, performed by specialized researchers in each field, without major inputs from clinical practitioners on the relevance of such strategy for future therapies. Translational research can shift the way these relationships operate towards a process in which new therapies can be generated by linking experimental discoveries directly to unmet clinical needs. Computational chemistry methods provide valuable insights on experimental findings and pharmacological and pathophysiological mechanisms, allow the virtual construction of new possibilities for the synthesis of new molecular entities, and pave the way for informed cost-effective decisions on expensive research projects. This text focus on the current computational methods used in drug design, how they can be used in a translational research model that starts from clinical practice and research-based theorization by medical practitioners and moves to applied research in a computational chemistry setting, aiming the development of new drugs for clinical use.

scholarly journals Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

2021 ◽  
Author(s):  
Abdelfatteh Haidine ◽  
Fatima Zahra Salmam ◽  
Abdelhak Aqqal ◽  
Aziz Dahbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Cost Effective ◽  
Mobile Internet ◽  
Network Function ◽  
New Radio ◽  
4G Lte

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

scholarly journals A machine learning-based system for detecting leishmaniasis in microscopic images

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Mojtaba Zare ◽  
Hossein Akbarialiabad ◽  
Hossein Parsaei ◽  
Qasem Asgari ◽  
Ali Alinejad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Visual Detection ◽  
Detection System ◽  
Cost Effective ◽  
Leishmania Parasite ◽  
Parasitic Disease ◽  
Integral Image ◽  
Parasite Detection ◽  
Image Creation

Abstract Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

Phytochemical composition of different plant parts of Acacia nilotica (L.) and their medicinal values

2021 ◽  
Vol 25 (7) ◽  
pp. 183-190
Author(s):  
Mansi Srivastva ◽  
Gargi Singh ◽  
Laxmi Parwani ◽  
Jaspreet Singh
Keyword(s):  
Therapeutic Potential ◽  
Herbal Medicines ◽  
Cost Effective ◽  
Therapeutic Index ◽  
Acacia Nilotica ◽  
New Drugs ◽  
Tropical Regions ◽  
Plant Parts ◽  
Phytochemical Composition ◽  
Antiplatelet Aggregation

Plant-derived medicines are long being used for the prevention and treatment of various human ailments. For the last few decades, plants are widely being explored for their active ingredients due to their immense potential in the treatment of critical illnesses. Thus, in recent years, exponential growth can be seen in the field of herbal medicines. Medicinal plants are a unique source of valuable phytochemicals. Their use in different medicine systems is gradually increasing due to their cost-effectiveness, easy availability and natural origin with fewer or no side effects. Acacia nilotica (L.) is a member of the family Fabaceae, commonly found in tropical and sub-tropical regions and the plant is widely known for its enormous medicinal values. Every plant part of A. nilotica is a source of many bioactive important secondary metabolites that are widely useful for the cure of various human diseases and the development of new drugs. An exhaustive literature survey revealed that tannins, flavonoids, alkaloids, polyphenols, fatty acids and carbohydrates are present as major classes of phytochemicals in different plant parts of A. nilotica. These phytochemicals exhibit significant antioxidant, anti-inflammatory, antibacterial, antifungal, antidiarrheal, antihypertensive, antispasmodic, anthelmintic, antiplatelet aggregation, anticancer and antiviral activities. The present review is aimed to organize the comprehensive information available on phytochemical composition and medicinal properties of different plant parts of A. nilotica viz. leaves, bark, flowers, seeds, pods, gum and roots. The study is useful to explore the therapeutic potential of different plant parts of A. nilotica which will further help in the development of new promising, safe, cost-effective drugs with a high therapeutic index from the different parts of the Acacia plant.

scholarly journals Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach

2020 ◽  
Vol 36 (16) ◽  
pp. 4490-4497
Author(s):  
Siqi Liang ◽  
Haiyuan Yu
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Drug Targets ◽  
Class Imbalance ◽  
Target Prediction ◽  
Drug Repurposing ◽  
New Drugs ◽  
Supplementary Information ◽  
Training Scheme ◽  
Drug Target Prediction

Abstract Motivation In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Results Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug–target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Masateru Taniguchi ◽  
Shohei Minami ◽  
Chikako Ono ◽  
Rina Hamajima ◽  
Ayumi Morimura ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Speed ◽  
Simple Procedure ◽  
Rna Extraction ◽  
Disease Outbreaks ◽  
High Sensitivity ◽  
Cost Effective ◽  
Rt Pcr ◽  
Emerging Viruses

AbstractHigh-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

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