scholarly journals In Silico Research of New Therapeutics Rotenoids Derivatives against Leishmania amazonensis Infection

Biology ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 133
Author(s):  
Adrián Vicente-Barrueco ◽  
Ángel Carlos Román ◽  
Trinidad Ruiz-Téllez ◽  
Francisco Centeno
Keyword(s):  
In Silico ◽  
Combinatorial Library ◽  
Leishmania Amazonensis ◽  
New Drugs ◽  
Ethnobotanical Knowledge ◽  
Computer Aided Drug Design ◽  
High Prevalence ◽  
Computer Aided ◽  
Key Enzyme ◽  
The One

Yearly, 1,500,000 cases of leishmaniasis are diagnosed, causing thousands of deaths. To advance in its therapy, we present an interdisciplinary protocol that unifies ethnobotanical knowledge of natural compounds and the latest bioinformatics advances to respond to an orphan disease such as leishmaniasis and specifically the one caused by Leishmania amazonensis. The use of ethnobotanical information serves as a basis for the development of new drugs, a field in which computer-aided drug design (CADD) has been a revolution. Taking this information from Amazonian communities, located in the area with a high prevalence of this disease, a protocol has been designed to verify new leads. Moreover, a method has been developed that allows the evaluation of lead molecules, and the improvement of their affinity and specificity against therapeutic targets. Through this approach, deguelin has been identified as a good lead to treat the infection due to its potential as an ornithine decarboxylase (ODC) inhibitor, a key enzyme in Leishmania development. Using an in silico-generated combinatorial library followed by docking approaches, we have found deguelin derivatives with better affinity and specificity against ODC than the original compound, suggesting that this approach could be adapted for developing new drugs against leishmaniasis.

scholarly journals In Silico Research of New Therapeutics Rotenoids Derivatives Against Leishmania Amazonensis Infection

2021 ◽  
Author(s):  
Adrián Vicente-Barrueco ◽  
Ángel Carlos Román ◽  
Trinidad Ruiz-Téllez ◽  
Francisco Centeno
Keyword(s):  
Drug Design ◽  
In Silico ◽  
Combinatorial Library ◽  
Leishmania Amazonensis ◽  
New Drugs ◽  
Ethnobotanical Knowledge ◽  
Computer Aided Drug Design ◽  
Computer Aided ◽  
Key Enzyme ◽  
The One

Yearly, 1,500,000 cases of leishmaniasis are diagnosed, causing thousands of deaths. To advance in its therapy, we present an interdisciplinary protocol that unifies ethnobotanical knowledge of natural compounds and the latest bioinformatics advances to respond to an orphan disease such as leishmaniasis and specifically the one caused by Leishmania amazonensis. The use of ethnobotanical information serves as a basis for the development of new drugs, a field in which computer-aided drug design (CADD) has been a revolution. Taking this information from Amazonian communities, located in the area with the highest prevalence of this disease, a protocol has been designed to verify new leads. Moreover, a method has been developed that allows the evaluation of lead molecules, and the improvement of their affinity and specificity against therapeutic targets. Through this approach, deguelin has been identified as a good lead to treat the infection due to its potential as an ornithine decarboxylase (ODC) inhibitor, a key enzyme in Leishmania development. Using an in silico-generated combinatorial library followed by docking approaches, we have found deguelin derivatives with better affinity and specificity against ODC than the original compound, suggesting that this approach could be adapted for developing new drugs against leishmaniasis.

scholarly journals Advances in Applying Computer-Aided Drug Design for Neurodegenerative Diseases

2021 ◽  
Vol 22 (9) ◽  
pp. 4688
Author(s):  
Mootaz M. Salman ◽  
Zaid Al-Obaidi ◽  
Philip Kitchen ◽  
Andrea Loreto ◽  
Roslyn M. Bill ◽  
...  
Keyword(s):  
Drug Design ◽  
Neurodegenerative Diseases ◽  
Docking Studies ◽  
New Drugs ◽  
Computer Aided Drug Design ◽  
Biological Assays ◽  
Advantages And Disadvantages ◽  
Computer Aided ◽  
Research Challenge ◽  
The Cost

Neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

scholarly journals Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases

Marine Drugs ◽  
2022 ◽  
Vol 20 (1) ◽  
pp. 53
Author(s):  
Laura Llorach-Pares ◽  
Alfons Nonell-Canals ◽  
Conxita Avila ◽  
Melchor Sanchez-Martinez
Keyword(s):  
Natural Products ◽  
Drug Design ◽  
Therapeutic Potential ◽  
Marine Invertebrate ◽  
In Silico Analysis ◽  
New Drugs ◽  
Computer Aided Drug Design ◽  
Marine Habitats ◽  
Computer Aided ◽  
Promising Line

Computer-aided drug design (CADD) techniques allow the identification of compounds capable of modulating protein functions in pathogenesis-related pathways, which is a promising line on drug discovery. Marine natural products (MNPs) are considered a rich source of bioactive compounds, as the oceans are home to much of the planet’s biodiversity. Biodiversity is directly related to chemodiversity, which can inspire new drug discoveries. Therefore, natural products (NPs) in general, and MNPs in particular, have been used for decades as a source of inspiration for the design of new drugs. However, NPs present both opportunities and challenges. These difficulties can be technical, such as the need to dive or trawl to collect the organisms possessing the compounds, or biological, due to their particular marine habitats and the fact that they can be uncultivable in the laboratory. For all these difficulties, the contributions of CADD can play a very relevant role in simplifying their study, since, for example, no biological sample is needed to carry out an in-silico analysis. Therefore, the amount of natural product that needs to be used in the entire preclinical and clinical study is significantly reduced. Here, we exemplify how this combination between CADD and MNPs can help unlock their therapeutic potential. In this study, using a set of marine invertebrate molecules, we elucidate their possible molecular targets and associated therapeutic potential, establishing a pipeline that can be replicated in future studies.

Development of novel therapeutics for the treatment of glaucoma based on actin-binding kinase inhibition – in silico approach

2020 ◽  
Vol 44 (17) ◽  
pp. 6923-6931 ◽  
Author(s):  
Maja Zivkovic ◽  
Marko Zlatanovic ◽  
Nevena Zlatanovic ◽  
Jasmina Djordjevic Jocic ◽  
Mladjan Golubović ◽  
...  
Keyword(s):  
Drug Design ◽  
In Silico ◽  
Actin Binding ◽  
Qsar Modeling ◽  
Computer Aided Drug Design ◽  
Kinase Inhibition ◽  
Novel Therapeutics ◽  
Computer Aided ◽  
Glaucoma Treatment

QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for glaucoma treatment.

scholarly journals Integration of Cassandra and Spark in Computer Aided Drug Design

2021 ◽  
pp. 68-73
Author(s):  
Nitha V R
Keyword(s):  
Big Data ◽  
Molecular Modeling ◽  
Drug Design ◽  
Programming Languages ◽  
New Drugs ◽  
Java Virtual Machine ◽  
Management Tool ◽  
Computer Aided Drug Design ◽  
Modeling Tool ◽  
Computer Aided

The primary purpose of this paper is to provide feasibility study of Cassandra and spark in Computer Aided Drug Design (CADD). The Apache Cassandra database is a big data management tool which can be used to store huge amount of data in different file formats. A huge database can be designed with details of all known molecules or compounds that are existing on earth. The information regarding the compounds such as selectivity, solubility, synthetic viability, affinity, adverse reactions, metabolism and environmental toxicity along with the 3 D structure of molecule can be stored in this big database. A data analytics tool “spark” can be efficiently used in mining and managing huge data stored in the database. Integrating big data in CADD helps in identifying the candidate drugs within minutes, not years. It may take eight to fifteen years to develop a new drug traditionally. Spark is written in Scala Programming Language which runs on Java Virtual Machine (JVM) and it supports Scala, Java and Python Programming languages .Cassandra can provide connectors to different programming languages, hence it’s very easy to integrate any other molecular modeling tool with Spark. A python based molecular modeling tool called Pymol can be easily implemented with Spark. CADD helps in identifying new drugs by computational means thus eliminating unnecessary cost incurred in chemical testing of drugs.

scholarly journals Фармакофорне моделювання діуретичної активності похідних 1,3-тіазолу та 1,3,4-тіадіазолу

2019 ◽  
Vol 13 (3) ◽  
pp. 197-202
Author(s):  
І. В. Драпак
Keyword(s):  
Drug Design ◽  
In Silico ◽  
Operating Environment ◽  
Computer Aided Drug Design ◽  
Molecular Operating Environment ◽  
Computer Aided

Фармакофорне моделювання є одним з перспективних напрямів комп’ютерної підтримки розробки ліків (Computer-Aided Drug Design). Цей метод дозволяє встановити набір та взаємне розташування специфічних молекулярних фрагментів, які необхідні для прояву біологічної активності. Мета дослідження – фармакофорне моделювання похідних 1,3-тіазолу та 1,3,4-тіадіазолу для цілеспрямованого пошуку потенційних діуретиків. Для моделювання фармакофора, відповідального за прояв діуретичної активності в ряду досліджених сполук, використовували комп’ютерну програму MOE (Molecular Operating Environment). Фармакофорне моделювання ряду похідних тіазолу та тіадіазолу як потенційних діуретиків, активність яких встановлена in vivo, дало змогу виділити можливий фармакофор, що складається з ароматичного кільця, двох проекцій акцептора водневого зв’язку та однієї проекції донора водневого зв’язку. Точність класифікації активних і неактивних сполук даною моделлю становить 0,74, дана модель може надалі застосовуватись для in silico скринінгу молекулярних баз даних з метою ідентифікації віртуальних хітів. Порівнюючи узгодженість N-(5-метил-1,3,4-тіадіазол-2-іл)пропанаміду та ацетазоламіду з розробленою фармакофорною моделлю, визначено модулюючий вплив замісника в 5-му положенні тіадіазольного кільця на силу діуретичного ефекту in vivo. Отримані дані свідчать, що замісники з позитивним індуктивним і мезомерним ефектом у цьому положенні можуть сприяти кращій афінності досліджуваних молекул до їхньої біомішені (якою з найбільшою ймовірністю є карбоангідраза). Подальші модифікації фрагментів у 5-му положенні тіадіазольного скаффолду є перспективним напрямом для розширення бібліотеки потенційних діуретиків.

scholarly journals Binding of Inhibitors to the Monomeric and Dimeric SARS-CoV-2 Mpro

2020 ◽  
Author(s):  
Nguyen Minh Tam ◽  
Pham Cam Nam ◽  
Duong Tuan Quang ◽  
Nguyen Thanh Tung ◽  
Van Vu ◽  
...  
Keyword(s):  
Effective Treatment ◽  
In Silico ◽  
Monomeric Form ◽  
In Silico Screening ◽  
Computer Aided Drug Design ◽  
Main Protease ◽  
Study Results ◽  
Computer Aided ◽  
Speed Up ◽  
Monomeric Structure

<div> <p>SARS-CoV-2 rapidly infects millions of people worldwide since December 2019. There is still no effective treatment for the virus, resulting in the death of more than one million of patients. Inhibiting the activity of SARS-CoV-2 main protease (Mpro), 3C-like protease (3CLP), is able to block the viral replication and proliferation. In this context, our study has revealed that in silico screening for inhibitors of SARS-CoV-2 Mpro can be reliably done using the monomeric structure of the Mpro instead of the dimeric one. Docking and fast pulling of ligand (FPL) simulations for both monomeric and dimeric forms correlate well with the corresponding experimental binding affinity data of 30 compounds. The obtained results were also confirmed via binding pose and noncovalent contact analyses. Our study results show that it is possible to speed up computer-aided drug design for SARS-CoV-2 Mpro by focusing on the monomeric form instead of the larger dimeric one.</p></div>

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