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2.
Int J Mol Sci ; 23(24)2022 Dec 15.
Article in English | MEDLINE | ID: covidwho-20245403

ABSTRACT

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 µM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.


Subject(s)
Drug Discovery , Software , Molecular Docking Simulation , Proteins , Ligands , Protein Binding
3.
Sci Rep ; 13(1): 9204, 2023 06 06.
Article in English | MEDLINE | ID: covidwho-20242518

ABSTRACT

The recent outbreak of the COVID-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has shown the necessity for fast and broad drug discovery methods to enable us to react quickly to novel and highly infectious diseases. A well-known SARS-CoV-2 target is the viral main 3-chymotrypsin-like cysteine protease (Mpro), known to control coronavirus replication, which is essential for the viral life cycle. Here, we applied an interaction-based drug repositioning algorithm on all protein-compound complexes available in the protein database (PDB) to identify Mpro inhibitors and potential novel compound scaffolds against SARS-CoV-2. The screen revealed a heterogeneous set of 692 potential Mpro inhibitors containing known ones such as Dasatinib, Amodiaquine, and Flavin mononucleotide, as well as so far untested chemical scaffolds. In a follow-up evaluation, we used publicly available data published almost two years after the screen to validate our results. In total, we are able to validate 17% of the top 100 predictions with publicly available data and can furthermore show that predicted compounds do cover scaffolds that are yet not associated with Mpro. Finally, we detected a potentially important binding pattern consisting of 3 hydrogen bonds with hydrogen donors of an oxyanion hole within the active side of Mpro. Overall, these results give hope that we will be better prepared for future pandemics and that drug development will become more efficient in the upcoming years.


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , SARS-CoV-2/metabolism , Pandemics , Antiviral Agents/pharmacology , Antiviral Agents/chemistry , Protease Inhibitors/pharmacology , Protease Inhibitors/chemistry , Molecular Docking Simulation , Viral Nonstructural Proteins/metabolism , Drug Discovery/methods
4.
Microbiol Spectr ; 11(3): e0118623, 2023 Jun 15.
Article in English | MEDLINE | ID: covidwho-2325934

ABSTRACT

SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, is a highly contagious positive-sense RNA virus. Its explosive community spread and the emergence of new mutant strains have created palpable anxiety even in vaccinated people. The lack of effective anticoronavirus therapeutics continues to be a major global health concern, especially due to the high evolution rate of SARS-CoV-2. The nucleocapsid protein (N protein) of SARS-CoV-2 is highly conserved and involved in diverse processes of the virus replication cycle. Despite its critical role in coronavirus replication, N protein remains an unexplored target for anticoronavirus drug discovery. Here, we demonstrate that a novel compound, K31, binds to the N protein of SARS-CoV-2 and noncompetitively inhibits its binding to the 5' terminus of the viral genomic RNA. K31 is well tolerated by SARS-CoV-2-permissive Caco2 cells. Our results show that K31 inhibited SARS-CoV-2 replication in Caco2 cells with a selective index of ~58. These observations suggest that SARS-CoV-2 N protein is a druggable target for anticoronavirus drug discovery. K31 holds promise for further development as an anticoronavirus therapeutic. IMPORTANCE The lack of potent antiviral drugs for SARS-CoV-2 is a serious global health concern, especially with the explosive spread of the COVID-19 pandemic worldwide and the constant emergence of new mutant strains with improved human-to-human transmission. Although an effective coronavirus vaccine appears promising, the lengthy vaccine development processes in general and the emergence of new mutant viral strains with a potential to evade the vaccine always remain a serious concern. The antiviral drugs targeted to the highly conserved targets of viral or host origin remain the most viable and timely approach, easily accessible to the general population, in combating any new viral illness. The majority of anticoronavirus drug development efforts have focused on spike protein, envelope protein, 3CLpro, and Mpro. Our results show that virus-encoded N protein is a novel therapeutic target for anticoronavirus drug discovery. Due to its high conservation, the anti-N protein inhibitors will likely have broad-spectrum anticoronavirus activity.


Subject(s)
COVID-19 , Humans , SARS-CoV-2 , COVID-19 Vaccines , Pandemics/prevention & control , Caco-2 Cells , Drug Discovery , Antiviral Agents/therapeutic use , Nucleocapsid Proteins
5.
Nat Rev Drug Discov ; 22(7): 585-603, 2023 Jul.
Article in English | MEDLINE | ID: covidwho-2320224

ABSTRACT

During the coronavirus disease 2019 (COVID-19) pandemic, a wave of rapid and collaborative drug discovery efforts took place in academia and industry, culminating in several therapeutics being discovered, approved and deployed in a 2-year time frame. This article summarizes the collective experience of several pharmaceutical companies and academic collaborations that were active in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antiviral discovery. We outline our opinions and experiences on key stages in the small-molecule drug discovery process: target selection, medicinal chemistry, antiviral assays, animal efficacy and attempts to pre-empt resistance. We propose strategies that could accelerate future efforts and argue that a key bottleneck is the lack of quality chemical probes around understudied viral targets, which would serve as a starting point for drug discovery. Considering the small size of the viral proteome, comprehensively building an arsenal of probes for proteins in viruses of pandemic concern is a worthwhile and tractable challenge for the community.


Subject(s)
COVID-19 , Animals , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , SARS-CoV-2 , Drug Discovery , Pandemics
6.
Curr Pharm Des ; 29(15): 1180-1192, 2023 Jun 06.
Article in English | MEDLINE | ID: covidwho-2319521

ABSTRACT

Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.


Subject(s)
Artificial Intelligence , COVID-19 , Humans , Drug Discovery/methods , Machine Learning , Algorithms , Drug Design
7.
Molecules ; 28(9)2023 May 05.
Article in English | MEDLINE | ID: covidwho-2312914

ABSTRACT

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.


Subject(s)
Computer-Aided Design , Drug Design , Drug Discovery/methods , Computers , Pharmaceutical Preparations
8.
Int J Mol Sci ; 24(9)2023 Apr 29.
Article in English | MEDLINE | ID: covidwho-2312525

ABSTRACT

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.


Subject(s)
Artificial Intelligence , COVID-19 Drug Treatment , Coronavirus 3C Proteases , Drug Discovery , Small Molecule Libraries , Models, Molecular , Small Molecule Libraries/pharmacology , Small Molecule Libraries/therapeutic use , Coronavirus 3C Proteases/antagonists & inhibitors , Drug Discovery/methods , Neural Networks, Computer
9.
Front Cell Infect Microbiol ; 13: 1157627, 2023.
Article in English | MEDLINE | ID: covidwho-2290774

ABSTRACT

Background: In the last couple of years, viral infections have been leading the globe, considered one of the most widespread and extremely damaging health problems and one of the leading causes of mortality in the modern period. Although several viral infections are discovered, such as SARS CoV-2, Langya Henipavirus, there have only been a limited number of discoveries of possible antiviral drug, and vaccine that have even received authorization for the protection of human health. Recently, another virial infection is infecting worldwide (Monkeypox, and Smallpox), which concerns pharmacists, biochemists, doctors, and healthcare providers about another epidemic. Also, currently no specific treatment is available against Monkeypox. This research gap encouraged us to develop a new molecule to fight against monkeypox and smallpox disease. So, firstly, fifty different curcumin derivatives were collected from natural sources, which are available in the PubChem database, to determine antiviral capabilities against Monkeypox and Smallpox. Material and method: Preliminarily, the molecular docking experiment of fifty different curcumin derivatives were conducted, and the majority of the substances produced the expected binding affinities. Then, twelve curcumin derivatives were picked up for further analysis based on the maximum docking score. After that, the density functional theory (DFT) was used to determine chemical characterizations such as the highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), softness, and hardness, etc. Results: The mentioned derivatives demonstrated docking scores greater than 6.80 kcal/mol, and the most significant binding affinity was at -8.90 kcal/mol, even though 12 molecules had higher binding scores (-8.00 kcal/mol to -8.9 kcal/mol), and better than the standard medications. The molecular dynamic simulation is described by root mean square deviation (RMSD) and root-mean-square fluctuation (RMSF), demonstrating that all the compounds might be stable in the physiological system. Conclusion: In conclusion, each derivative of curcumin has outstanding absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics. Hence, we recommended the aforementioned curcumin derivatives as potential antiviral agents for the treatment of Monkeypox and Smallpox virus, and more in vivo investigations are warranted to substantiate our findings.


Subject(s)
COVID-19 , Curcumin , Monkeypox , Smallpox , Variola virus , Humans , Smallpox/drug therapy , Curcumin/pharmacology , Antiviral Agents/pharmacology , Molecular Docking Simulation , Drug Design , Drug Discovery , Molecular Dynamics Simulation
10.
PLoS One ; 18(4): e0282042, 2023.
Article in English | MEDLINE | ID: covidwho-2298613

ABSTRACT

A computational approach to identifying drug-target interactions (DTIs) is a credible strategy for accelerating drug development and understanding the mechanisms of action of small molecules. However, current methods to predict DTIs have mainly focused on identifying simple interactions, requiring further experiments to understand mechanism of drug. Here, we propose AI-DTI, a novel method that predicts activatory and inhibitory DTIs by combining the mol2vec and genetically perturbed transcriptomes. We trained the model on large-scale DTIs with MoA and found that our model outperformed a previous model that predicted activatory and inhibitory DTIs. Data augmentation of target feature vectors enabled the model to predict DTIs for a wide druggable targets. Our method achieved substantial performance in an independent dataset where the target was unseen in the training set and a high-throughput screening dataset where positive and negative samples were explicitly defined. Also, our method successfully rediscovered approximately half of the DTIs for drugs used in the treatment of COVID-19. These results indicate that AI-DTI is a practically useful tool for guiding drug discovery processes and generating plausible hypotheses that can reveal unknown mechanisms of drug action.


Subject(s)
COVID-19 , Transcriptome , Humans , Drug Discovery/methods , Drug Interactions
11.
Comput Biol Med ; 158: 106881, 2023 05.
Article in English | MEDLINE | ID: covidwho-2297843

ABSTRACT

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.


Subject(s)
Machine Learning , Transcriptome , Transcriptome/genetics , Drug Discovery/methods , Proteins/chemistry , Gene Regulatory Networks
12.
Viruses ; 15(2)2023 02 19.
Article in English | MEDLINE | ID: covidwho-2296067

ABSTRACT

Despite the great technological and medical advances in fighting viral diseases, new therapies for most of them are still lacking, and existing antivirals suffer from major limitations regarding drug resistance and a limited spectrum of activity. In fact, most approved antivirals are directly acting antiviral (DAA) drugs, which interfere with viral proteins and confer great selectivity towards their viral targets but suffer from resistance and limited spectrum. Nowadays, host-targeted antivirals (HTAs) are on the rise, in the drug discovery and development pipelines, in academia and in the pharmaceutical industry. These drugs target host proteins involved in the virus life cycle and are considered promising alternatives to DAAs due to their broader spectrum and lower potential for resistance. Herein, we discuss an important class of HTAs that modulate signal transduction pathways by targeting host kinases. Kinases are considered key enzymes that control virus-host interactions. We also provide a synopsis of the antiviral drug discovery and development pipeline detailing antiviral kinase targets, drug types, therapeutic classes for repurposed drugs, and top developing organizations. Furthermore, we detail the drug design and repurposing considerations, as well as the limitations and challenges, for kinase-targeted antivirals, including the choice of the binding sites, physicochemical properties, and drug combinations.


Subject(s)
Antiviral Agents , Protein Kinases , Humans , Antiviral Agents/pharmacology , Drug Repositioning , Drug Discovery , Drug Design
13.
Int J Mol Sci ; 24(7)2023 Mar 24.
Article in English | MEDLINE | ID: covidwho-2304189

ABSTRACT

The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) infections is one of the most crucial challenges currently faced by the scientific community. Developments in the fundamental understanding of their underlying mechanisms may open new perspectives in drug discovery. In this review, we conducted a systematic literature search in PubMed, Web of Science, and Scopus, to collect information on innovative strategies to hinder iron acquisition in bacteria. In detail, we discussed the most interesting targets from iron uptake and metabolism pathways, and examined the main chemical entities that exhibit anti-infective activities by interfering with their function. The mechanism of action of each drug candidate was also reviewed, together with its pharmacodynamic, pharmacokinetic, and toxicological properties. The comprehensive knowledge of such an impactful area of research will hopefully reflect in the discovery of newer antibiotics able to effectively tackle the antimicrobial resistance issue.


Subject(s)
Anti-Bacterial Agents , Anti-Infective Agents , Anti-Bacterial Agents/pharmacology , Anti-Bacterial Agents/therapeutic use , Anti-Infective Agents/pharmacology , Anti-Infective Agents/therapeutic use , Bacteria , Drug Discovery , Iron
14.
Int J Mol Sci ; 24(7)2023 Mar 30.
Article in English | MEDLINE | ID: covidwho-2303895

ABSTRACT

The development of a new drug from the first hit to the launch of an approved product is a complex process that usually take around 12-15 years and costs more than USD 1-2 billion [...].


Subject(s)
Drug Discovery
15.
Nat Commun ; 14(1): 1177, 2023 03 01.
Article in English | MEDLINE | ID: covidwho-2299944

ABSTRACT

Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly curated dataset of 39 experimentally confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures likely contain cryptic pockets, vastly expanding the potentially druggable proteome.


Subject(s)
Labor, Obstetric , Proteome , Humans , Pregnancy , Female , Drug Discovery , Molecular Dynamics Simulation , Neural Networks, Computer
16.
Future Med Chem ; 14(18): 1325-1340, 2022 09.
Article in English | MEDLINE | ID: covidwho-2275963

ABSTRACT

Although synthetic biology is an emerging research field, which has come to prominence within the last decade, it already has many practical applications. Its applications cover the areas of pharmaceutical biotechnology and drug discovery, bringing essential novel methods and strategies such as metabolic engineering, reprogramming the cell fate, drug production in genetically modified organisms, molecular glues, functional nucleic acids and genome editing. This review discusses the main avenues for synthetic biology application in pharmaceutical biotechnology. The authors believe that synthetic biology will reshape drug development and drug production to a similar extent as the advances in organic chemical synthesis in the 20th century. Therefore, synthetic biology already plays an essential role in pharmaceutical, biotechnology, which is the main focus of this review.


Subject(s)
Metabolic Engineering , Synthetic Biology , Biotechnology , Drug Discovery , Pharmaceutical Preparations
17.
J Clin Psychopharmacol ; 42(6): 518-522, 2022.
Article in English | MEDLINE | ID: covidwho-2260473

ABSTRACT

BACKGROUND: Current psychiatric drug discovery and development has not produced very effective medications in the past few decades. Conventional wisdom provides reasons for failure that do not address major structural obstacles to true innovation for psychiatric drugs. METHOD: Narrative review based on analysis of the scientific literature augmented by personal experience in academic clinical research as well as in the pharmaceutical industry. RESULTS: The largest obstacles to drug discovery and development are the biological invalidity of most DSM diagnoses, the economic incentives to produce short-term symptomatic treatments with blockbuster profit potential, and very low thresholds set by the FDA for ending drug discovery due to toxicity. Since these larger structural socio-economic obstacles to drug development will be difficult to change, a new proposal is made for a parallel non-profit drug discovery paradigm, to be funded by governments, akin to the development of vaccines for the Covid-19 pandemic. The key public health implications are highlighted in the example of developing new drugs for Alzheimer dementia, and the potential utility of an anti-tau agent like lithium, currently ignored in drug development in favor of much more expensive and questionably effective amyloid-reducing agents. CONCLUSIONS: Given the key structural problems of psychiatric drug discovery and development, a parallel non-profit drug discovery paradigm is needed to meet all public health needs, as well as to reinvigorate truly innovative and transformative research.


Subject(s)
COVID-19 Vaccines , COVID-19 , Humans , Drug Discovery , Drug Industry , Drug Development
18.
SLAS Discov ; 28(2): 1-2, 2023 03.
Article in English | MEDLINE | ID: covidwho-2289252
19.
Appl Biochem Biotechnol ; 195(6): 3653-3670, 2023 Jun.
Article in English | MEDLINE | ID: covidwho-2283392

ABSTRACT

COVID-19 infection is a new disease and our knowledge is limited; day in and day out more and more interesting yet diverse observations are reported by the different research groups from different corners of the world. So, there is an urgent requirement of the invention of some effective and efficient drugs that can carry out the end of the deadly viral infection. Throughout the world, there have been many efforts carried out in different labs to invent such a drug and also identifying any pre-existing drugs which can carry out the killing of the virus. In this review, an effort has been made to understand the potential drugs which can be used against the SARS-CoV-2 viral infection. Again, the strategies on the current and the future drug discovery mechanisms against the SARS-CoV-2 are also mentioned. The different drugs made and the drugs re-used and also the drugs which are in the making process in different research laboratories across the world are also mentioned. To combat this unexpected crisis, we still need some more efforts from the different scientific communities around the world for finding a cure against this viral infection and this is needed to be done for the prevention of more loss of human life.


Subject(s)
COVID-19 , Humans , SARS-CoV-2 , Antiviral Agents/therapeutic use , Drug Repositioning , Drug Discovery
20.
Biochemistry (Mosc) ; 88(1): 64-72, 2023 Jan.
Article in English | MEDLINE | ID: covidwho-2282385

ABSTRACT

Medicinal plants and their therapeutically promising chemical compounds belonging to the valued category of 'traditional medicine' are potential remedies for various health problems. Due to their complex structure and enormous health benefits, the high-value plant-derived metabolites collectively termed as 'phytochemicals' have emerged as a crucial source for novel drug discovery and development. Indeed, several medicinal plants from diverse habitats are still in the 'underexplored' category in terms of their bioactive principles and therapeutic potential. COVID-19, infection caused by the SARS-CoV-2, first reported in November 2019, resulted in the alarming number of deaths (6.61 million), was further declared 'pandemic', and spread of the disease has continued till today. Even though the well-established scientific world has successfully implemented vaccines against COVID-19 within the short period of time, the focus on alternative remedies for long-term symptom management and immunity boosting have been increased. At this point, interventions based on traditional medicine, which include medicinal plants, their bioactive metabolites, extracts and formulations, attracted a lot of attention as alternative solutions for COVID-19 management. Here, we reviewed the recent research findings related to the effectiveness of phytochemicals in treatment or prevention of COVID-19. Furthermore, the literature regarding the mechanisms behind the preventive or therapeutic effects of these natural phytochemicals were also discussed. In conclusion, we suggest that the active plant-derived components could be used alone or in combination as an alternative solution for the management of SARS-CoV-2 infection. Moreover, the structure of these natural productomes may lead to the emergence of new prophylactic strategies for SARS-CoV-2-caused infection.


Subject(s)
COVID-19 , Humans , SARS-CoV-2 , COVID-19 Vaccines , Drug Discovery , Phytochemicals/pharmacology , Phytochemicals/therapeutic use , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use
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