scholarly journals TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches

Molecules ◽  
2020 ◽  
Vol 25 (3) ◽  
pp. 627 ◽  
Author(s):  
Qurat ul Ain ◽  
Maria Batool ◽  
Sangdun Choi
Keyword(s):  
Drug Discovery ◽  
Lupus Erythematosus ◽  
Drug Targets ◽  
Pharmacophore Modeling ◽  
Biological Data ◽  
Structural Basis ◽  
Computational Techniques ◽  
Relationship Analysis ◽  
Computer Aided ◽  
Pharmacologically Active

The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer’s disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.

scholarly journals An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Arun Bahadur Gurung ◽  
Mohammad Ajmal Ali ◽  
Joongku Lee ◽  
Mohammad Abul Farah ◽  
Khalid Mashay Al-Anazi
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Drug Targets ◽  
Pharmacophore Modeling ◽  
Review Article ◽  
Drug Candidate ◽  
Computational Techniques ◽  
Computer Aided Drug Design ◽  
Structure Based Drug Design ◽  
Computer Aided

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection

2019 ◽  
Vol 20 (5) ◽  
pp. 522-539 ◽  
Author(s):  
Surovi Saikia ◽  
Manobjyoti Bordoloi ◽  
Rajeev Sarmah
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Phase I ◽  
Human Genome ◽  
Phase Ii ◽  
Drug Targets ◽  
Complex Diseases ◽  
Phase Iii ◽  
Computational Techniques ◽  
Receptor Complexes

The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including “off-target” effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.

Computational techniques for designing new lead molecules in the process of drug discovery

2017 ◽  
Vol 3 (01) ◽  
Author(s):  
Suresh Kumar ◽  
Samiyara Begum ◽  
Hemant Kumar Srivastava
Keyword(s):  
Drug Discovery ◽  
Quantitative Structure Activity Relationship ◽  
Pharmacophore Modeling ◽  
Computational Techniques ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Qsar Studies ◽  
Structure Activity ◽  
Sampling Algorithms

Computational techniques are important in the field of drug discovery. These techniques are generally categorized in two methods namely ‘structure-based’ and ‘ligand-based’ methods. The present review discusses the theory of the most important methods, recent successful applications, pharmacophore modeling and quantitative structure-activity relationship (QSAR) studies. A brief introduction of molecular docking methods and their development and applications in drug discovery process is also included. Basic theories and fundamental techniques including sampling algorithms and scoring functions are discussed.

Screening of Natural Lead Molecules Against Putative Molecular Targets of Drug-resistant Cryptococcus spp: An Insight from Computer-aided Molecular Design

2019 ◽  
Vol 18 (31) ◽  
pp. 2681-2701
Author(s):  
Meghna Manjunath ◽  
Sinosh Skariyachan
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Targets ◽  
Medicinal Chemistry ◽  
Fungal Infections ◽  
Limited Information ◽  
New Paradigm ◽  
Effective Prevention ◽  
Computer Aided

Cryptococcosis is one of the major invasive fungal infections distributed worldwide with high mortality rate. C. neoformans and C. gattii are the major organisms that cause various types of infections. Anti-fungal resistances exhibited by the mentioned species of Cryptococcus threaten their effective prevention and treatment. There is limited information available on human to human transmission of the pathogen and virulent factors that are responsible for Cryptococcus mediated infections. Hence, there is high scope for understanding the mechanism, probable drug targets and scope of developing natural therapeutic agents that possess high relevance to pharmaceutical biotechnology and medicinal chemistry. The proposed review illustrates the role of computer-aided virtual screening for the screening of probable drug targets and identification of natural lead candidates as therapeutic remedies. The review initially focuses on the current perspectives on cryptococcosis, major metabolic pathways responsible for the pathogenesis, conventional therapies and associated drug resistance, challenges and scope of structure-based drug discovery. The review further illustrates various approaches for the prediction of unknown drug targets, molecular modeling works, screening of natural compounds by computational virtual screening with ideal drug likeliness and pharmacokinetic features, application of molecular docking studies and simulation. Thus, the present review probably provides AN insight into the role of medicinal chemistry and computational drug discovery to combat Cryptococcus infections and thereby open a new paradigm for the development of novel natural therapeutic against various drug targets for cryptococcal infections.

Exploring molecular approaches in Amyotrophic lateral sclerosis: Drug targets from clinical and pre-clinical findings

2020 ◽  
Vol 13 ◽  
Author(s):  
Mamtaj Alam ◽  
Rajeshwar Kumar Yadav ◽  
Elizabeth Minj ◽  
Aarti Tiwari ◽  
Sidharth Mehan
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Motor Neurons ◽  
Drug Targets ◽  
Therapeutic Index ◽  
Experimental Models ◽  
Clinical Findings ◽  
Adenyl Cyclase ◽  
Molecular Approaches ◽  
Pharmacologically Active ◽  
Lateral Sclerosis

: Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease (MND) characterised by the death of upper and lower motor neurons (corticospinal tract) in the motor cortex, basal ganglia, brain stem, and spinal cord. The patient experiences the sign and symptoms between 55 to 75 years of age included impaired motor movement, difficulty in speaking and swallowing, grip loss, muscle atrophy, spasticity and sometimes associated with memory and cognitive impairments. Median survival is 3 to 5 years after diagnosis and 5 to 10% beyond 10 years of age. The limited intervention of pharmacologically active compounds that are used clinically is majorly associated with the narrow therapeutic index. Pre-clinically established experimental models where neurotoxin methyl mercury mimics the ALS like behavioural and neurochemical alterations in rodents associated with neuronal mitochondrial dysfunctions and downregulation of adenyl cyclase mediated cAMP/CREB is the main pathological hallmark for the progression of ALS in central as well in the peripheral nervous system. Despite the considerable investigation into neuroprotection, it still constrains treatment choices to strong care and organization of ALS complications. Therefore, current review specially targeted in the investigation of clinical and pre-clinical features available for ALS to understand the pathogenic mechanisms and to explore the pharmacological interventions associated with up-regulation of intracellular adenyl cyclase/cAMP/CREB and mitochondrial-ETC coenzyme-Q10 activation as a future drug target in the amelioration of ALS mediated motor neuronal dysfunctions.

Mathematical and Computational Techniques for Drug Discovery: Promises and Developments

2019 ◽  
Vol 18 (32) ◽  
pp. 2774-2799 ◽  
Author(s):  
Krishnan Balasubramanian
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Quantum Chemical ◽  
Intrinsically Disordered Proteins ◽  
Hepatitis Virus ◽  
Disordered Proteins ◽  
Computational Techniques ◽  
Large Set ◽  
Structural Protein ◽  
Intrinsically Disordered

We review various mathematical and computational techniques for drug discovery exemplifying some recent works pertinent to group theory of nested structures of relevance to phylogeny, topological, computational and combinatorial methods for drug discovery for multiple viral infections. We have reviewed techniques from topology, combinatorics, graph theory and knot theory that facilitate topological and mathematical characterizations of protein-protein interactions, molecular-target interactions, proteomics, genomics and statistical data reduction procedures for a large set of starting chemicals in drug discovery. We have provided an overview of group theoretical techniques pertinent to phylogeny, protein dynamics especially in intrinsically disordered proteins, DNA base permutations and related algorithms. We consider computational techniques derived from high level quantum chemical computations such as QM/MM ONIOM methods, quantum chemical optimization of geometries complexes, and molecular dynamics methods for providing insights into protein-drug interactions. We have considered complexes pertinent to Hepatitis Virus C non-structural protein 5B polymerase receptor binding of C5-Arylidebne rhodanines, complexes of synthetic potential vaccine molecules with dengue virus (DENV) and HIV-1 virus as examples of various simulation studies that exemplify the utility of computational tools. It is demonstrated that these combinatorial and computational techniques in conjunction with experiments can provide promising new insights into drug discovery. These techniques also demonstrate the need to consider a new multiple site or allosteric binding approach to drug discovery, as these studies reveal the existence of multiple binding sites.

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

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