scholarly journals Geometric Hashing and ΑΙ-Quantum Deep Learning functional similarities on Remdesivir, drug synergies to treat COVID-19 in Practice.

2021 ◽  
Author(s):  
Ioannis Grigoriadis
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Large Scale ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Superior Performance ◽  
Viral Agent ◽  
Innovative Drug ◽  
Modern Drug ◽  
Small Molecule Libraries

Νovel SARS coronavirus 2 (SARS-CoV-2) of the family Coronaviridae starting in China and spreading around the world is an enveloped, positive-sense, single-stranded RNA of the genus betacoronavirus encoding the SARS-COV-2 (2019-NCOV, Coronavirus Disease 2019. Remdesivir drug, or GS-5734 lead compound, first described in 2016 as a potential anti-viral agent for Ebola diseade and has also being researched as a potential therapeutic agent against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus that causes coronavirus disease 2019 (COVID-19). Computer-aided drug design (CADD), Structure and Ligand based Drug Repositioning strategies based on parallel docking methodologies have been widely used for both modern drug development and drug repurposing to find effective treatments against this disease. Quantum mechanics, molecular mechanics, molecular dynamics (MD), and combinations have shown superior performance to other drug design approaches providing an unprecedented opportunity in the rational drug development fields and for the developing of innovative drug repositioning methods. We tested 18 phytochemical small molecule libraries and predicted their synergies in COVID-19 (2019- NCOV), to devise therapeutic strategies, repurpose existing ones in order to counteract highly pathogenic SARS-CoV-2 infection. We anticipate that our geometry hashing driven quantum deep learing similarity approaches which is based on separated pairs of short consecutive matching fragments, can be used for the development of anticoronaviral drug combinations in large scale HTS screenings, and to maximize the safety and efficacy of the Remdesivir, Colchicine and Ursolic acid drugs already known to induce synergy with potential therapeutic value or drug repositioning to COVID-19 patients.

scholarly journals Quantum Deep Learning Functional Similarities on Remdesivir, Drug Synergies to Treat COVID-19 in Practice.

2020 ◽  
Author(s):  
Ioannis Grigoriadis
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Large Scale ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Superior Performance ◽  
Viral Agent ◽  
Innovative Drug ◽  
Modern Drug ◽  
Small Molecule Libraries

Abstract Νovel SARS coronavirus 2 (SARS-CoV-2) of the family Coronaviridae starting in China and spreading around the world is an enveloped, positive-sense, single-stranded RNA of the genus betacoronavirus encoding the SARS-COV-2 (2019-NCOV, Coronavirus Disease 2019. Remdesivir drug, or GS-5734 lead compound, first described in 2016 as a potential anti-viral agent for Ebola diseade and has also being researched as a potential therapeutic agent against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus that causes coronavirus disease 2019 (COVID-19). Computer-aided drug design (CADD), Structure and Ligand based Drug Repositioning strategies based on parallel docking methodologies have been widely used for both modern drug development and drug repurposing to find effective treatments against this disease. Quantum mechanics, molecular mechanics, molecular dynamics (MD), and combinations have shown superior performance to other drug design approaches providing an unprecedented opportunity in the rational drug development fields and for the developing of innovative drug repositioning methods. We tested 18 phytochemical small molecule libraries and predicted their synergies in COVID-19 (2019- NCOV), to devise therapeutic strategies, repurpose existing ones in order to counteract highly pathogenic SARS-CoV-2 infection and the BRD4- conserved residue associated COVID-19 pathology. We anticipate that our quantum deep learing similarity approaches can be used for the development of anticoronaviral drug combinations in large scale HTS screenings, and to maximize the safety and efficacy of the Remdesivir, Colchicine and Ursolic acid drugs already known to induce synergy with potential therapeutic value or drug repositioning to COVID-19 patients.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

2015 ◽  
pp. 1-47 ◽  
Author(s):  
Maryam Hamzeh-Mivehroud ◽  
Babak Sokouti ◽  
Siavoush Dastmalchi
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Biological Activities ◽  
Model Development ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
New Drugs ◽  
Qsar Modeling ◽  
Qsar Studies ◽  
Modern Drug

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

Oncology ◽  
2017 ◽  
pp. 20-66
Author(s):  
Maryam Hamzeh-Mivehroud ◽  
Babak Sokouti ◽  
Siavoush Dastmalchi
Keyword(s):  
Drug Design ◽  
Drug Development ◽  
Biological Activities ◽  
Model Development ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
New Drugs ◽  
Qsar Modeling ◽  
Qsar Studies ◽  
Modern Drug

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

scholarly journals ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 12
Author(s):  
Peter D. Karp ◽  
Bonnie Berger ◽  
Diane E. Kovats ◽  
Thomas Lengauer ◽  
Michal Linial ◽  
...  
Keyword(s):  
Computational Biology ◽  
Intelligent Systems ◽  
Large Scale ◽  
Ebola Virus ◽  
Docking Studies ◽  
Future Research ◽  
Computational Approaches ◽  
Modern Drug ◽  
Small Molecule Libraries ◽  
Human Proteins

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database.Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, Florida).

Antidepressants and antipsychotic agents as repurposable oncological drug candidates

2020 ◽  
Vol 27 ◽  
Author(s):  
Michał Antoszczak ◽  
Anna Markowska ◽  
Janina Markowska ◽  
Adam Huczyński
Keyword(s):  
Drug Development ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Antipsychotic Agents ◽  
Therapeutic Action ◽  
Drug Candidates ◽  
New Strategy ◽  
The Central Nervous System ◽  
Medical Indications

: Drug repurposing, known also as drug repositioning/reprofiling, is a relatively new strategy for identification of alternative uses of well-known therapeutics that are outside the scope of their original medical indications. Such an approach might entail a number of advantages compared to standard de novo drug development, including less time needed to introduce the drug to the market, and lower costs. The group of compounds that could be considered as promising candidates for repurposing in oncology includes the central nervous system drugs, especially selected antidepressant and antipsychotic agents. In this article, we provide an overview of some antidepressants (citalopram, fluoxetine, paroxetine, sertraline) and antipsychotics (chlorpromazine, pimozide, thioridazine, trifluoperazine) that have the potential to be repurposed as novel chemotherapeutics in cancer treatment, as they have been found to exhibit preventive and/or therapeutic action in cancer patients. Nevertheless, although drug repurposing seems to be an attractive strategy to search for oncological drugs, we would like to clearly indicate that it should not replace the search for new lead structures, but only complement de novo drug development.

scholarly journals Drug Repurposing: A Review

2021 ◽  
pp. 161-169
Author(s):  
Rani Teksinh Bhagat ◽  
Santosh Ramarao Butle
Keyword(s):  
Drug Development ◽  
Research And Development ◽  
Development Process ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
New Drugs ◽  
Preclinical Development ◽  
Therapeutic Uses ◽  
Original Application ◽  
Approved Drugs

The drug development is a very time consuming and complex process. Drug development Process is Expensive. Success rate for the new drug development is very small. In recent years, decreases the new drugs development. The powerful tools are developed to support the research and development (R&D) process is essential. The Drug repurposing are helpful for research and development process. The drug re-purposing as an approach finds new therapeutic uses for current candidates or existing candidates or approved drugs, different from its original application. The main aimed of Drug repurposing is to reduce costs and research time investments in Research & Development. It is used for the diagnosis and treatment of various diseases. Repositioning is important over traditional approaches and need for effective therapies. Drug re-purposing identifies new application for already banned or existing drugs from market. In drug design, drug repurposing plays important role, because it helps to preclinical development. It reducing time efforts, expenses and failures in drug discovery process. It is also called as drug repositioning, drug redirecting, drug reprofiling.

scholarly journals In-Silico Drug Design: A revolutionary approach to change the concept of current Drug Discovery Process

2013 ◽  
Vol 1 (02) ◽  
pp. 60-73 ◽  
Author(s):  
Lakhyajit Boruah ◽  
Aparoop Das ◽  
Lalit Mohan Nainwal ◽  
Neha Agarwal ◽  
Brajesh Shankar
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
In Silico ◽  
Traditional Approach ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Chemical Library ◽  
In Silico Drug Design ◽  
Modern Drug ◽  
Small Molecule Libraries

Computational methods play a central role in modern drug discovery process. It includes the design and management of small molecule libraries, initial hit identification through virtual screening, optimization of the affinity as well as selectivity of hits and improving the physicochemical properties of the lead compounds. In this review article, computational drug designing approaches have been elucidated and discussed. The key considerations and guidelines for virtual chemical library design and whole drug discovery process. Traditional approach for discovery of a new drug is a costly and time consuming affair besides not being so productive. A number of potential reasons witness choosing the In-silico method of drug design to be a more wise and productive approach. There is a general perception that applied science has not kept pace with the advances of basic science. Therefore, there is a need for the use of alternative tools to get answers on efficacy and safety faster, with more certainty and at lower cost. In-silico drug design can play a significant role in all stages of drug development from the initial lead designing to final stage clinical development.

scholarly journals Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Guohua Huang ◽  
Yin Lu ◽  
Changhong Lu ◽  
Mingyue Zheng ◽  
Yu-Dong Cai
Keyword(s):  
Drug Development ◽  
Large Scale ◽  
Cross Validation ◽  
Drug Repositioning ◽  
Accuracy Rate ◽  
Computational Approaches ◽  
Starting Point ◽  
Independent Test ◽  
Approved Drugs ◽  
Fold Cross Validation

Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.

scholarly journals Drug Repurposing Strategy (DRS): Emerging Approach to Identify Potential Therapeutics for Treatment of Novel Coronavirus Infection

2021 ◽  
Vol 8 ◽  
Author(s):  
Biswa Mohan Sahoo ◽  
B. V. V. Ravi Kumar ◽  
J. Sruti ◽  
Manoj Kumar Mahapatra ◽  
Bimal K. Banik ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Development ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Therapeutic Agents ◽  
Free Energy Calculations ◽  
Pharmacokinetic Property ◽  
The Novel ◽  
Drug Candidates ◽  
Drug Molecules

Drug repurposing is also termed as drug repositioning or therapeutic switching. This method is applied to identify the novel therapeutic agents from the existing FDA approved clinically used drug molecules. It is considered as an efficient approach to develop drug candidates with new pharmacological activities or therapeutic properties. As the drug discovery is a costly, time-consuming, laborious, and highly risk process, the novel approach of drug repositioning is employed to increases the success rate of drug development. This strategy is more advantageous over traditional drug discovery process in terms of reducing duration of drug development, low-cost, highly efficient and minimum risk of failure. In addition to this, World health organization declared Coronavirus disease (COVID-19) as pandemic globally on February 11, 2020. Currently, there is an urgent need to develop suitable therapeutic agents for the prevention of the outbreak of COVID-19. So, various investigations were carried out to design novel drug molecules by utilizing different approaches of drug repurposing to identify drug substances for treatment of COVID-19, which can act as significant inhibitors against viral proteins. It has been reported that COVID-19 can infect human respiratory system by entering into the alveoli of lung via respiratory tract. So, the infection occurs due to specific interaction or binding of spike protein with angiotensin converting enzyme-2 (ACE-2) receptor. Hence, drug repurposing strategy is utilized to identify suitable drugs by virtual screening of drug libraries. This approach helps to determine the binding interaction of drug candidates with target protein of coronavirus by using computational tools such as molecular similarity and homology modeling etc. For predicting the drug-receptor interactions and binding affinity, molecular docking study and binding free energy calculations are also performed. The methodologies involved in drug repurposing can be categorized into three groups such as drug-oriented, target-oriented and disease or therapy-oriented depending on the information available related to quality and quantity of the physico-chemical, biological, pharmacological, toxicological and pharmacokinetic property of drug molecules. This review focuses on drug repurposing strategy applied for existing drugs including Remdesivir, Favipiravir, Ribavirin, Baraticinib, Tocilizumab, Chloroquine, Hydroxychloroquine, Prulifloxacin, Carfilzomib, Bictegravir, Nelfinavir, Tegobuvir and Glucocorticoids etc to determine their effectiveness toward the treatment of COVID-19.

scholarly journals A drug repurposing screen identifies hepatitis C antivirals as inhibitors of the SARS-CoV2 main protease

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245962
Author(s):  
Jeremy D. Baker ◽  
Rikki L. Uhrich ◽  
Gerald C. Kraemer ◽  
Jason E. Love ◽  
Brian C. Kraemer
Keyword(s):  
Hepatitis C ◽  
Drug Development ◽  
Large Scale ◽  
Previous History ◽  
Drug Repurposing ◽  
Lead Compounds ◽  
Main Protease ◽  
History Of ◽  
History Of Use

Effective SARS-CoV-2 antiviral drugs are desperately needed. The SARS-CoV-2 main protease (Mpro) appears as an attractive target for drug development. We show that the existing pharmacopeia contains many drugs with potential for therapeutic repurposing as selective and potent inhibitors of SARS-CoV-2 Mpro. We screened a collection of ~6,070 drugs with a previous history of use in humans for compounds that inhibit the activity of Mpro in vitro and found ~50 compounds with activity against Mpro. Subsequent dose validation studies demonstrated 8 dose responsive hits with an IC50 ≤ 50 μM. Hits from our screen are enriched with hepatitis C NS3/4A protease targeting drugs including boceprevir, ciluprevir. narlaprevir, and telaprevir. This work suggests previous large-scale commercial drug development initiatives targeting hepatitis C NS3/4A viral protease should be revisited because some previous lead compounds may be more potent against SARS-CoV-2 Mpro than boceprevir and suitable for rapid repurposing.

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