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 Recent Advances in In Silico Target Fishing

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5124 ◽  
Author(s):  
Salvatore Galati ◽  
Miriana Di Stefano ◽  
Elisa Martinelli ◽  
Giulio Poli ◽  
Tiziano Tuccinardi
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Targets ◽  
Biological Activities ◽  
Protein Structures ◽  
Drug Repurposing ◽  
Specific Protein ◽  
Protein Targets ◽  
Drug Candidates ◽  
Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

scholarly journals Identification of a novel inhibitor for the Bromodomain (BRD4) through a receptor-based drug discovery approach

2020 ◽  
Vol 11 (3) ◽  
pp. 4359-4364
Author(s):  
Arivukkarasi Varadharajan ◽  
Raman Rajeshkumar ◽  
Chandrasekar MJN
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Molecular Level ◽  
Autodock Vina ◽  
Rule Violation ◽  
Docking Analysis ◽  
Novel Target ◽  
The Right ◽  
Almost All ◽  
Derivatives Of

Recently, the demands on the drug discovery process have increased drastically because of the need to apprehend a novel target which might be both pertinent to cause disease and chemically tractable. The emergence of bioinformatics and computer strategies have given room to analyse conditions at the molecular level. The present work was to perform a molecular docking analysis and ADMET study of different δ-carboline derivatives with bromodomain (BRD4) receptor using receptor-based drug discovery approach. Based on the literature, 60 compounds were designed and subjected to molecular docking for the inhibition of brd4 receptor. The results showed that Compound 34 received the highest binding affinity with BRD4 receptor. Hence eight compounds were selected based on docked pose determined using AutoDock/Vina with the minimal energy of above -5.1. Then ADMET study was carried, in that, all the eight compounds had middle to high BBB permeability. During metabolism, all compounds except compounds 37, 42 and 47 showed no inhibition of CYP2C99 in the liver. Analysis of drug-likeness profile showed almost all compounds eligible in CMC rule, violation rule of CMC, MDDR rule with the value of 1 and violations of WDI showed 0 value. Such findings strongly implied that derivatives of δ-carboline could serve as lead molecules to inhibit BRD4, and this could lead to the future development of the right candidate for cancer research.

scholarly journals Drug Repurposing: A Paradigm Shift in Drug Discovery

2020 ◽  
Vol 5 (04) ◽  
pp. 60-68
Author(s):  
Saravanan Jayaram ◽  
Emdormi Rymbai ◽  
Deepa Sugumar ◽  
Divakar Selvaraj
Keyword(s):  
Drug Discovery ◽  
Success Rate ◽  
Chemical Structure ◽  
Target Identification ◽  
Drug Repurposing ◽  
New Drugs ◽  
Attractive Option ◽  
Traditional Drug ◽  
Repurposed Drugs

The traditional methods of drug discovery and drug development are a tedious, complex, and costly process. Target identification, target validation; lead identification; and lead optimization are a lengthy and unreliable process that further complicates the discovery of new drugs. A study of more than 15 years reports that the success rate in the discovery of new drugs in the fields of ophthalmology, cardiovascular, infectious disease, and oncology to be 32.6%, 25.5%, 25.2% and 3.4%, respectively. A tedious and costly process coupled with a very low success rate makes the traditional drug discovery a less attractive option. Therefore, an alternative to traditional drug discovery is drug repurposing, a process in which already existing drugs are repurposed for conditions other than which were originally intended. Typical examples of repurposed drugs are thalidomide, sildenafil, memantine, mirtazapine, mifepristone, etc. In recent times, several databases have been developed to hasten drug repurposing based on the side effect profile, the similarity of chemical structure, and target site. This work reviews the pivotal role of drug repurposing in drug discovery and the databases currently available for drug repurposing.

scholarly journals Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

2020 ◽  
Author(s):  
Michael F. Cuccarese ◽  
Berton A. Earnshaw ◽  
Katie Heiser ◽  
Ben Fogelson ◽  
Chadwick T. Davis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Drug Repurposing ◽  
Rapid Identification ◽  
Large Molecules ◽  
Tnf Α ◽  
Genetic Perturbations ◽  
Compound Screening ◽  
Model Drug

ABSTRACTDevelopment of accurate disease models and discovery of immune-modulating drugs is challenged by the immune system’s highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable “phenomics” platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-β- and TNF-α-driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

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 SAR and Pharmacophore Based Designing of Some Antimalarial and Antiretroviral Agents: An INTERNET Based Drug Design Approach

2013 ◽  
Vol 11 (3 and 4) ◽  
Author(s):  
Soumendranath Bhakat
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Drug Design ◽  
Computational Chemistry ◽  
Operating Systems ◽  
Docking Studies ◽  
Computational Tools ◽  
Structure Activity ◽  
System Configurations ◽  
User Friendly

With the development of computational chemistry and molecular docking studies, Structure Activity Relationship or SAR- and pharmacophore-based drug design have been modified to target based drug discovery using sophisticated computational tools which is not very much user friendly and has got many incompatibility issues with many operating systems (OS) and other system configurations. In this paper SAR and pharmacophore based drug design approaches have been described by the used of free internet based tools which are very much user friendly and can almost compatible with any platform. Some antimalarial. And anti retroviral agents have been designed using pharmacophore study and their drug like properties, toxicity, metabolic sites and other parameters are predicted by the free internet based tools.

Estimating environmental pollution by xenobiotic chemicals using QSAR (QSBR) models based on artificial intelligence

1998 ◽  
Vol 37 (8) ◽  
pp. 9-18 ◽  
Author(s):  
B. Kompare
Keyword(s):  
Artificial Intelligence ◽  
Chemical Structure ◽  
Quantitative Structure ◽  
Topological Properties ◽  
Environmental Persistence ◽  
Structure Activity ◽  
Connectivity Indices ◽  
Quantitative Structure Activity Relationships ◽  
The Given ◽  
Xenobiotic Chemicals

An attempt was made to construct QSAR (Quantitative Structure-Activity Relationships) or QSBR (Quantitative Structure-Biodegradation Relationships) formulae and models for predicting biodegradability of chemicals in aqueous aerobic environment with machine learning (ML) tools of artificial intelligence (AI). Inverse of biodegradability is environmental persistence, from which possible dynamics of soil, groundwater and water pollution can be inferred. We tried to predict the biodegradability with several programs that can learn from examples and construct decision or regression trees and/or can construct equations. Besides the given basic topological properties, the main contribution was inclusion of connectivity indices. Above all, normalization of properties to molecular weight or the number of carbon atoms significantly improved prediction. The obtained results are comparable (or better) to the best achieved results with other approaches. Contrary to the statistical methods, ML tools present the information (learned knowledge) in a compact, easily understandable manner which can help identify and understand the key properties of chemicals and mechanisms important for assessing biodegradation (and thus possible environmental contamination) from chemical structure only.

Validating ADME QSAR Models Using Marketed Drugs

2021 ◽  
pp. 247255522110175
Author(s):  
Vishal Siramshetty ◽  
Jordan Williams ◽  
Ðắc-Trung Nguyễn ◽  
Jorge Neyra ◽  
Noel Southall ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Chemical Structure ◽  
Scientific Literature ◽  
Aqueous Solubility ◽  
Quantitative Structure Activity Relationship ◽  
Artificial Membrane ◽  
Structure Activity ◽  
Qsar Models ◽  
Permeability Assay ◽  
Tier I

Problems with drug ADME are responsible for many clinical failures. By understanding the ADME properties of marketed drugs and modeling how chemical structure contributes to these inherent properties, we can help new projects reduce their risk profiles. Kinetic aqueous solubility, the parallel artificial membrane permeability assay (PAMPA), and rat liver microsomal stability constitute the Tier I ADME assays at the National Center for Advancing Translational Sciences (NCATS). Using recent data generated from in-house lead optimization Tier I studies, we update quantitative structure–activity relationship (QSAR) models for these three endpoints and validate in silico performance against a set of marketed drugs (balanced accuracies range between 71% and 85%). Improved models and experimental datasets are of direct relevance to drug discovery projects and, together with the prediction services that have been made publicly available at the ADME@NCATS web portal ( https://opendata.ncats.nih.gov/adme/ ), provide important tools for the drug discovery community. The results are discussed in light of our previously reported ADME models and state-of-the-art models from scientific literature. Graphical Abstract [Figure: see text]

scholarly journals Navigating Chemical Space By Interfacing Generative Artificial Intelligence and Molecular Docking

2020 ◽  
Author(s):  
Ziqiao Xu ◽  
Orrette Wauchope ◽  
Aaron T. Frank
Keyword(s):  
Artificial Intelligence ◽  
Molecular Docking ◽  
Active Site ◽  
Chemical Space ◽  
Drug Repurposing ◽  
Open Science ◽  
Physiochemical Properties ◽  
Experimental Drugs ◽  
Main Protease

Here we report the testing and application of a simple, structure-aware framework to design target-specific screening libraries for drug development. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to rapidly explore chemical space conditioned on the unique physiochemical properties of the active site of a biomolecular target. As a proof-of-concept, we used our framework to construct a focused library for cyclin-dependent kinase type-2 (CDK2). We then used it to rapidly generate a library specific to the active site of the main protease (Mpro) of the SARS-CoV-2 virus, which causes COVID-19. By comparing approved and experimental drugs to compounds in our library, we also identified six drugs, namely, Naratriptan, Etryptamine, Panobinostat, Procainamide, Sertraline, and Lidamidine, as possible SARS-CoV-2 Mpro targeting compounds and, as such, potential drug repurposing candidates. To complement the open-science COVID-19 drug discovery initiatives, we make our SARS-CoV-2 Mpro library fully accessible to the research community (https://github.com/atfrank/SARS-CoV-2).

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