Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Stephani Joy Y. Macalino
 + 3055 word(s) 3055 2021-10-27 04:55:09 |
2 format correction
Peter Tang
 Meta information modification 3055 2021-12-29 07:00:57 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Macalino, S.J. In Silico Strategies in Tuberculosis Drug Discovery. Encyclopedia. Available online: https://encyclopedia.pub/entry/17615 (accessed on 27 July 2024).
Macalino SJ. In Silico Strategies in Tuberculosis Drug Discovery. Encyclopedia. Available at: https://encyclopedia.pub/entry/17615. Accessed July 27, 2024.
Macalino, Stephani Joy. "In Silico Strategies in Tuberculosis Drug Discovery" Encyclopedia, https://encyclopedia.pub/entry/17615 (accessed July 27, 2024).
Macalino, S.J. (2021, December 29). In Silico Strategies in Tuberculosis Drug Discovery. In Encyclopedia. https://encyclopedia.pub/entry/17615
Macalino, Stephani Joy. "In Silico Strategies in Tuberculosis Drug Discovery." Encyclopedia. Web. 29 December, 2021.
In Silico Strategies in Tuberculosis Drug Discovery
Edit
This entry is adapted from the peer-reviewed paper 10.3390/molecules25030665

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics.

tuberculosis druggability docking pharmacophore MD simulation QSAR DFT

1. Introduction

In 1882, Mycobacterium tuberculosis (Mtb) was identified by Robert Koch as the causative agent of tuberculosis (TB), an infectious disease that continuous to be a relevant threat to global public health, especially in low- to middle-income countries. The pathogenesis of TB has several risk factors, including HIV infection, malnutrition, air pollution, type 2 diabetes, alcoholism, and smoking [1][2][3][4][5].
TB is encountered either as latent TB infection (LTBI), which is non-communicable and asymptomatic [6], or active TB, which is communicable and has symptoms such as fever, weight loss, productive cough, and hemoptysis [7]. Active infection is also classified depending on the strain: (1) drug-sensitive, (2) multidrug-resistant TB (MDR-TB), which is resistant to isoniazid and rifampicin, and (3) extensively drug-resistant TB (XDR-TB), which shows resistance to isoniazid, rifampicin, any fluoroquinolone, and aminoglycoside. Around 1.7 billion people are projected to suffer from LTBI and are at risk of progressing into active TB infection [8]. The World Health Organization (WHO) stated that active TB disease can be found in approximately 10 million people and has caused approximately 1.5 million deaths in 2018. An estimated half million individuals have rifampicin-resistant TB (RR-TB), of which 78% had MDR-TB. Furthermore, approximately 6.2% are suggested to have XDR-TB from these MDR cases [8].

2. Current Tuberculosis Management

One of the major challenges in managing TB is the estimated three million ‘missing’ individuals who have developed active infections but remained undetected or undiagnosed. TB can be deadly if not treated. With the help of conventional regimen, an estimated 58 million infected individuals were saved from 2000 to 2018. Global treatment outcome in 2017 shows a success rate of 85% for new TB cases and 56% for those with drug-resistant TB [8].

2.1. Latent Tuberculosis Infection

Treatment for LTBI are only provided for select groups that have a high risk of transitioning to active TB infection, including HIV-positive patients, people who were exposed to those with active TB, patients undergoing dialysis for end-stage renal disease, taking anti-tumor necrosis factor (TNF) medications, preparing for transplant surgery, or those with silicosis. Depending on whether it is beneficial or not, especially for children below 5 years of age, exposure to patients with active MDR-TB would require personalized treatment regimens and close observation. WHO recommended several different treatment regimens for LTBI, including 3 months of rifapentine and isoniazid, 3–4 months of isoniazid and rifampicin, 3–4 months of rifampicin, and 6–9 months of isoniazid [9][10]. While all these have established efficacy, poor patient compliance continues to be an issue especially with long treatment periods [9][10][11].

2.2. Active Drug-Sensitive Tuberculosis

In the last several decades, the treatment strategy for active drug-sensitive TB has not changed from the standard regimen of first-line drugs rifampicin, isoniazid, pyrazinamide and ethambutol (Figure 1) for the first 2 months continued by isoniazid and rifampicin for the next 4 months [12][13]. While this treatment procedure is highly efficacious and successful, its long duration primarily leads to poor patient compliance. This has long been an issue in TB management, necessitating monitoring protocols like the directly observed therapy (DOT), wherein a health professional directly supervises each dose intake [14]. Another issue brought about by the prolonged treatment is drug toxicity resulting in numerous adverse effects such as skin rash, gastrointestinal intolerance, neuropathy, arthralgia, increase in liver enzymes, hepatitis, immune thrombocytopaenia, agranulocytosis, haemolysis, renal failure, optic neuritis, and ototoxicity [15][16].
/media/item_content/202112/61cbf95725858molecules-25-00665-g001.png
Figure 1. First- and second-line drugs approved for tuberculosis treatment.

2.3. Multiple and Extensively Drug-Resistant Tuberculosis

Failure to complete the full TB regimen leads to disease relapse and drug resistance, which is more challenging to treat. A specific regimen can be designed depending on the resistance profile of the TB strain in a patient [17][18]. These treatments are often of longer duration (18 months or more) and utilize the more expensive second-line drugs (Figure 1) which have uncertain efficacy and high toxicity, resulting in poorer compliance and undesirable outcomes. To mitigate these issues, an updated seven-drug regimen guideline for the treatment of drug-resistant TB lasting 9 to 12 months was released by the WHO last 2016 [19].
With the increasing threat of treatment-resistant TB infection, a number of drugs have been fast-tracked to aid with the efforts in controlling TB worldwide. At the end of 2012, the US Food and Drug Administration (FDA) conferred accelerated approval to the drug bedaquiline for the treatment of resistant TB [20]. Bedaquiline’s anti-mycobacterial activity is due to its inhibition of the mycobacterial ATP synthase, a key enzyme in ATP synthesis, resulting in bacterial death. However, its use was shown to have an increased risk of death, thereby causing concerns about its approval. During clinical trials, roughly 11.4% of patients who took bedaquiline died as compared with 2.5% of those who took placebo treatments [21]. In 2014, the use of delamanid, a nitro-dihydro-imidazooxazole derivative, in the treatment of MDR-TB in adults was given conditional approval by the European Medicines Agency (EMA) [22]. Delamanid inhibits mycolic acid biosynthesis to block the formation of mycobacterial cell wall leading to improved drug permeation and more effective treatment [23]. Just recently, pretomanid in combination with bedaquiline and linezolid has also been approved by the FDA for treatment-resistant TB [24]. Pretomanid is a prodrug activated by nitroreductase, which reduces pretomanid’s imidazole ring to generate active metabolites. Specifically, a des-nitro metabolite leads to elevated levels of nitric oxide, which displays antimycobacterial activities due to its work as a poison for bacterial respiration under anaerobic conditions [25]. In aerobic conditions, it works like delaminid by targeting cell wall mycolic acid biosynthesis [26], and while there were several potential targets for this drug, its exact protein target is not yet known [27].
An increasing number of XDR-TB cases, such as in India, China, South Africa, Russia, and in eastern Europe, have proved difficult to treat even with the more intensive drug-resistant TB treatment regimen [18]. Novel therapeutics such as bedaquiline, delamanid, and pretomanid might help in curing these patients, though a suitable treatment regimen still has to be carefully designed. However, there is an additional difficulty in acquiring these drugs, especially in developing countries, resulting in a pool of patients that may remain untreated. Essentially, TB can be cured completely with the use of currently available and newly approved anti-tubercular drugs. However, difficulties in diagnosing and reporting infection, long treatment durations leading to drug toxicity and poor patient compliance, emergence of drug resistant strains, and limited acquisition of required treatment urgently necessitates the discovery and development of newer and effective drugs for TB.

3. Rise of Computer-Aided Drug Design in TB Drug Discovery

The drug discovery paradigm covers a wide range of fields, including biochemistry, chemical and structural biology, chem- and bioinformatics, computational chemistry, physical chemistry, organic synthesis, and others. The whole process entails large investments of time, money, and effort in order to produce promising candidates for the pipeline. Over the years, the drug discovery process for new antitubercular therapeutics have changed due to the increase in biological and chemical data, number of identified and validated targets, and advances in high-throughput screening technologies and software development. Moreover, the progress in data storage capacities, supercomputing powers, and parallel processing in the last several years allowed computer-aided drug design (CADD) to become an integral part of TB pharmaceutical research. This continuing expansion in computing power can soon potentially allow the exploration of the vast chemical space, thought to comprise of approximately 1060 organic molecules below 500 Da, in order to identify therapeutically interesting scaffolds [28]. Moreover, the boom in protein structural data, including over 150,000 macromolecular structures found in the Protein Data Bank (PDB, www.rcsb.org) [29], proved beneficial in elucidating important molecular and computational concepts for drug design studies. As with any other disease, TB has been the subject of continuous and numerous drug discovery studies, including thousands of published CADD investigations. Despite this, a paper by Ekins et al. noted gaps in the application of these methods in TB research [30], resulting in the slow output of candidates into the TB drug pipeline despite the apparent need and urgency for this disease. This suggests that more rigorous efforts are needed in TB drug discovery to maximize the advantages provided by computational tools.
Computational or in silico methods are knowledge-driven, rationally exploring available data to investigate protein function and design new molecular entities (NMEs) that can effectively regulate its behavior. Computational drug discovery approaches are generally divided into structure—(SBDD) and ligand-based drug design (LBDD), depending on the availability of structural data (Figure 2). However, it has been a common practice to integrate these methods in a complementary manner in order to increase the success rate of current drug discovery projects (Figure 2). SBDD requires the target’s three-dimensional (3D) structure to be able to examine and use the binding pocket for screening and design of suitable ligands, which can then be experimentally validated and optimized. In the absence of protein structural data, LBDD utilizes knowledge gained from a collection of diverse ligands with known activity to create predictive models for hit discovery and lead optimization [31]. Different types of SB and LB strategies, or a combination thereof, can be applied at different stages of TB drug discovery and development in order to alleviate the challenges involved with experimental methods. With the availability of TB genome and proteome, as well as abundant structural data, data mining and docking strategies can be employed for target identification. Virtual screening (VS) can then be applied to pick out the best potential candidates from a database containing millions of molecules for a chosen TB target. After validation of candidates, structure-activity or -property relationship (SAR/SPR) studies can be implemented to understand mechanism of action and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties in order to design compounds with better activity and pharmacokinetics. Data (both positive and negative results) taken from these investigations can be kept and used for further iteration and method optimization in the design of novel TB compounds. Both commercial and free software and webservers have been developed covering different SBDD and LBDD techniques, some of which are listed in Table 1.
/media/item_content/202112/61cbf980be087molecules-25-00665-g002.png
Figure 2. In silico tools that can be applied to TB drug design and development.
Table 1. Free and commercially available programs, webservers, and source codes for SBDD and LBDD.

Function

Software/

Webserver Name

Availability

Website

Comparative modeling

SWISS-MODEL [32]

Free webserver

https://swissmodel.expasy.org/

Structural geometry confirmation

MODELLER [33]

Free standalone program for academic license or commercially available through BIOVIA

https://salilab.org/modeller/

https://www.3dsbiovia.com/

Robetta [34]

Free webserver

http://new.robetta.org/

Prime [35]

Commercially available through Schrödinger

https://www.schrodinger.com/prime

I-TASSER [36][37][38][39][40][41]

Free webserver or standalone program for academic license

https://zhanglab.ccmb.med.umich.edu/I-TASSER/
 

HHPred [42][43][44]

Free webserver

https://toolkit.tuebingen.mpg.de/tools/hhpred

Structural geometry confirmation

PROCHECK [45]

Free webserver and source code

https://www.ebi.ac.uk/thornton-srv/software/PROCHECK/

Druggability and binding site prediction

Druggability and binding site prediction

ProSA [46]

Free webserver

https://prosa.services.came.sbg.ac.at/prosa.php

VERIFY3D [47]

Free webserver

https://servicesn.mbi.ucla.edu/Verify3D/

ERRAT [48]

Free webserver

https://servicesn.mbi.ucla.edu/ERRAT/

PockDrug [49]

Free webserver

http://pockdrug.rpbs.univ-paris-diderot.fr/cgi-bin/index.py?page=home

DoGSiteScorer [50]

Free webserver

https://proteins.plus/

fpocket [51][52]

Free/open source platform

https://github.com/Discngine/fpocket

CASTp [53][54][55]

Free webserver

http://sts.bioe.uic.edu/castp/calculation.html

PocketQuery [56]

Free webserver

http://pocketquery.csb.pitt.edu/

PASS [57]

Free/open source platform

http://www.ccl.net/cca/software/UNIX/pass/overview.html
 

SiteMap [58]

Commercially available through Schrödinger

https://www.schrodinger.com/sitemap

Docking, pharmacophore, and virtual screening

Docking, pharmacophore, and virtual screening

ConCavity [59]

Free webserver

https://compbio.cs.princeton.edu/concavity/

PrankWeb [60]

Free webserver

http://prankweb.cz/

ProFunc [61]

Free webserver

http://www.ebi.ac.uk/thornton-srv/databases/ProFunc/

AutoDock [62] and AutoDock Vina [63]

Free standalone program

http://autodock.scripps.edu/

DOCK [64]

Free/open source platform

http://dock.compbio.ucsf.edu/

GOLD [65]

Commercially available through CCDC

https://www.ccdc.cam.ac.uk/solutions/csd-discovery/components/gold/

Glide [66]

Commercially available through Schrödinger

https://www.schrodinger.com/glide/

Induced Fit [67]

Commercially available through Schrödinger

https://www.schrodinger.com/induced-fit
 

FlexX [68]

Commercially available through BioSolveIT

https://www.biosolveit.de/flexx/index.html

RosettaLigand [69]

Free/open source platform for academic license

https://www.rosettacommons.org/software

CDOCKER [70]

Commercially available through BIOVIA

https://www.3dsbiovia.com/

SwissDock [71][72]

Free webserver

http://www.swissdock.ch/docking

Pharmer [73]

Free/open source platform

http://smoothdock.ccbb.pitt.edu/pharmer/

CATALYST [74]

Commercially available through BIOVIA

https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/pharmacophore-and-ligand-based-design.html

PharmGist [75]

Free webserver

http://bioinfo3d.cs.tau.ac.il/pharma/php.php

LigandScout [76]

Commercially available through Inte:Ligand

http://www.inteligand.com/ligandscout/

SwissSimilarity [77]

Free webserver

http://www.swisssimilarity.ch/
 

LEA3D [78]

Free webserver

https://chemoinfo.ipmc.cnrs.fr/LEA3D/index.html

PyRx [79]

Free (no support) or commercially available

https://pyrx.sourceforge.io/

Phase [80]

Commercially available through Schrödinger

https://www.schrodinger.com/phase

Molecular Dynamics

AMBER [81][82]

Commercially available

https://ambermd.org/

CHARMM [83]

Free or commercially available through CHARMM or BIOVIA

http://charmm.chemistry.harvard.edu/

https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/simulations.html

CHARMMing [84]

Free webserver

https://www.charmming.org/charmming

GROMACS [85][86]

Free/open source platform

http://www.gromacs.org/

NAMD [87]

Free/open source platform

https://www.ks.uiuc.edu/Research/namd/

Desmond [88]

Commercially available through Schrödinger

https://www.schrodinger.com/desmond

SwissParam [89]

Free webserver

http://www.swissparam.ch/

CHARMM-GUI [90]

Free webserver

http://www.charmm-gui.org/

ParamChem CGenFF [91][92][93]

Free webserver

https://cgenff.umaryland.edu/

VMD [94]

Free/open source platform

https://www.ks.uiuc.edu/Research/vmd/

Molecular Descriptors, Fingerprints, and Quantitative Structure-Activity Relationship

Dragon [95]

Commercially available through Talete

http://www.talete.mi.it/products/dragon_description.htm

E-Dragon [96]

Free webserver

http://146.107.217.178/lab/edragon/start.html

Canvas [97]

Commercially available through Schrödinger

https://www.schrodinger.com/canvas

RDKit [98]

Free/open source platform

https://www.rdkit.org/docs/source/rdkit.ML.Descriptors.MoleculeDescriptors.html
 

PyDescriptor [99]

Free/open source platform

https://ochem.eu/home/show.do

Mordred [100]

Free/open source platform

https://github.com/mordred-descriptor/mordred

Open3DQSAR [101]

Free/open source platform

http://open3dqsar.sourceforge.net/?Home

ChemSAR [102]

Free webserver

http://chemsar.scbdd.com/

SeeSAR [103]

Commercially available through BioSolveIT

https://www.biosolveit.de/SeeSAR/

Pharmacokinetic properties

QikProp [104]

Commercially available through Schrödinger

https://www.schrodinger.com/qikprop

ADMET Predictor [105]

Commercially available through SimulationsPlus, Inc.

https://www.simulations-plus.com/software/overview/

ACD Percepta [106]

Commercially available through ACD/Labs

https://www.acdlabs.com/products/percepta/index.php

FAF-Drugs4 [107]

Free webserver

http://fafdrugs4.mti.univ-paris-diderot.fr/
 

PatchSearch [108]

Free webserver

http://mobyle.rpbs.univ-paris-diderot.fr/cgi-bin/portal.py#forms::PatchSearch
 

TOPKAT [109] and ADMET [110]

Commercially available through BIOVIA

https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/qsar-admet-and-predictive-toxicology.html

PASS Online [111]

Free webserver or commercially available standalone program

http://pharmaexpert.ru/Passonline/index.php

SwissADME [112]

Free webserver

http://www.swissadme.ch/

MetaSite [113]

Commercially available through Molecular Discovery

https://www.moldiscovery.com/software/metasite/

ToxPredict [114]

Free webserver

https://apps.ideaconsult.net/ToxPredict#

VirtualToxLab [115][116][117][118]

Free standalone software

http://www.biograf.ch/index.php?id=home

admetSAR [119][120][121]

Free webserver

http://lmmd.ecust.edu.cn/admetsar1/home/

MetaTox [122][123]

Free webserver

http://way2drug.com/mg2/

More available tools and detailed descriptions for the programs and servers can be found at https://www.click2drug.org/.

4. Edges and Pitfalls of In Silico Methods

There are roughly 2500 protein structures for tuberculosis in the PDB and perhaps thousands of ligand candidates published. All these pieces of information are available with a few keyboard strokes and a click of the mouse. Along with existing technologies, it is now possible to analyze TB enzymes and lead candidates at the atomic level in order to understand their function and how to regulate them. While computational methods have been widely used in drug discovery nowadays due to their successful applications [124][125][126], it is still important to remember that these tools are like any other experimental approaches—prone to limitations dependent on the system and other various parameters being studied [127][128][129].
VS has been known to successfully screen millions of compounds to identify potential inhibitors for a given target [124][126]. This lends efficiency to cost, time, and efforts used in drug discovery projects as only the most promising compounds are brought forward for more rigorous experimental testing and drug development. However, optimization and validation of these methods are far from perfect and are highly dependent on the protein system and compound classes used, leading to possible bias in the computational model. Thus, it is challenging to determine which method has the advantage over another; many benchmark studies have been published regarding this matter [130][131]. Other major limitations include difficulties in incorporating protein flexibility and solvent effects due to the computational burden attached to these factors [31]. Fortunately, available technologies seem to be catching up as enhance sampling methods, HPC, and MD platforms are now routinely applied in drug discovery projects and are known to calculate up to milliseconds of simulations for various protein targets [132][133][134]. In terms of ligand-based drug design, its main advantage is its simplicity and efficiency. Indeed, LBDD has a long history and numerous candidates have already been discovered even with the lack of protein structural information [135][136][137]. Nonetheless, several factors should be considered when applying ligand-based tools. Firstly, ligand alignments are based on the lowest conformation energy, which is often different from the bioactive conformation [138][139], as well as on the assumption that ligands bind in the same site and display the same conformation. Secondly, compounds should be evaluated by the same group (preferred) or tested using the same assay with the same parameters to be considered comparable [140]. Thirdly, the basic premise of ‘similar structures display similar activities’ are contradicted by the existence of activity cliffs [141][142][143], and so care should be taken when selecting potential candidates from a pool of virtual hits. Finally, it is also a challenge to incorporate the effects of solvation and protein flexibility due to the nature of the analysis.
As mentioned in the previous section, integration of several in silico methods have become common practice when designing and optimizing lead candidates to overcome the shortcomings of each individual tools. Despite requiring more computational resources, assimilation of computational methods result in better accuracy and enrichment of hits. In addition, the combination of a researcher’s innate knowledge with the computational efficiency of these tools is perhaps the best integration of all, as a human’s touch continues to be irreplaceable in the interpretation of all the data produced by in silico methods.

References

  1. Pai, M.; Behr, M.A.; Dowdy, D.; Dheda, K.; Divangahi, M.; Boehme, C.C.; Ginsberg, A.; Swaminathan, S.; Spigelman, M.; Getahun, H.; et al. Tuberculosis. Nat. Rev. Dis. Primers 2016, 2, 16076.
  2. Havlir, D.V.; Getahun, H.; Sanne, I.; Nunn, P. Opportunities and challenges for HIV care in overlapping HIV and TB epidemics. JAMA 2008, 300, 423–430.
  3. Leung, C.C.; Yew, W.W.; Chan, C.K.; Chang, K.C.; Law, W.S.; Lee, S.N.; Tai, L.B.; Leung, E.C.; Au, R.K.; Huang, S.S.; et al. Smoking adversely affects treatment response, outcome and relapse in tuberculosis. Eur. Respir. J. 2015, 45, 738–745.
  4. Imtiaz, S.; Shield, K.D.; Roerecke, M.; Samokhvalov, A.V.; Lonnroth, K.; Rehm, J. Alcohol consumption as a risk factor for tuberculosis: Meta-analyses and burden of disease. Eur. Respir. J. 2017, 50, 1700216.
  5. Restrepo, B.I. Diabetes and Tuberculosis. In Understanding the Host Immune Response against Mycobacterium tuberculosis Infection; Springer International Publishing: Cham, Switzerland, 2018; pp. 1–21.
  6. Getahun, H.; Matteelli, A.; Chaisson, R.E.; Raviglione, M. Latent Mycobacterium tuberculosis infection. N. Engl. J. Med. 2015, 372, 2127–2135.
  7. Miller, L.G.; Asch, S.M.; Yu, E.I.; Knowles, L.; Gelberg, L.; Davidson, P. A population-based survey of tuberculosis symptoms: How atypical are atypical presentations? Clin. Infect. Dis. 2000, 30, 293–299.
  8. World Health Organization (WHO). Global Tuberculosis Report 2019; World Health Organization (WHO): Geneva, Switzerland, 2019.
  9. World Health Organization (WHO). Guidelines on the Management of Latent Tuberculosis Infection; World Health Organization (WHO): Geneva, Switzerland, 2015.
  10. World Health Organization (WHO). Latent Tuberculosis infection: Updated and Consolidated Guidelines for Programmatic Management; World Health Organization (WHO): Geneva, Switzerland, 2018.
  11. Getahun, H.; Matteelli, A.; Abubakar, I.; Aziz, M.A.; Baddeley, A.; Barreira, D.; Den Boon, S.; Borroto Gutierrez, S.M.; Bruchfeld, J.; Burhan, E.; et al. Management of latent Mycobacterium tuberculosis infection: WHO guidelines for low tuberculosis burden countries. Eur. Respir. J. 2015, 46, 1563–1576.
  12. World Health Organization (WHO). Treatment of Tuberculosis: Guidelines; World Health Organization (WHO): Geneva, Switzerland, 2010.
  13. Nahid, P.; Dorman, S.E.; Alipanah, N.; Barry, P.M.; Brozek, J.L.; Cattamanchi, A.; Chaisson, L.H.; Chaisson, R.E.; Daley, C.L.; Grzemska, M.; et al. Official American Thoracic Society/Centers for Disease Control and Prevention/Infectious Diseases Society of America Clinical Practice Guidelines: Treatment of Drug-Susceptible Tuberculosis. Clin. Infect. Dis. 2016, 63, e147–e195.
  14. Volmink, J.; Garner, P. Directly observed therapy for treating tuberculosis. Cochrane Database Syst. Rev. 2007, CD003343.
  15. Horsburgh, C.R., Jr.; Barry, C.E., 3rd; Lange, C. Treatment of Tuberculosis. N. Engl. J. Med. 2015, 373, 2149–2160.
  16. Saukkonen, J.J.; Cohn, D.L.; Jasmer, R.M.; Schenker, S.; Jereb, J.A.; Nolan, C.M.; Peloquin, C.A.; Gordin, F.M.; Nunes, D.; Strader, D.B.; et al. An official ATS statement: Hepatotoxicity of antituberculosis therapy. Am. J. Respir. Crit. Care Med. 2006, 174, 935–952.
  17. Dheda, K.; Barry, C.E., 3rd; Maartens, G. Tuberculosis. Lancet 2016, 387, 1211–1226.
  18. Dheda, K.; Gumbo, T.; Gandhi, N.R.; Murray, M.; Theron, G.; Udwadia, Z.; Migliori, G.B.; Warren, R. Global control of tuberculosis: From extensively drug-resistant to untreatable tuberculosis. Lancet. Respir. Med. 2014, 2, 321–338.
  19. World Health Organization (WHO). WHO Treatment Guidelines for Drug-Resistant Tuberculosis 2016 Update; World Health Organization (WHO): Geneva, Switzerland, 2016.
  20. Walker, J.; Tadena, N. J&J Tuberculosis Drug Gets Fast-Track Clearance. Wall St. J. 2013. Available online: https://www.wsj.com/articles/SB10001424127887323320404578213421059138236 (accessed on 27 November 2019).
  21. Mahajan, R. Bedaquiline: First FDA-approved tuberculosis drug in 40 years. Int. J. Appl. Basic Med. Res. 2013, 3, 1–2.
  22. European Medicines Agency (EMA). Deltyba Delamanid Summary of the European Public Assessment Report (EPAR) for Deltyba; EMA: Amsterdam, The Netherlands, 2014; pp. 1–3. Available online: https://www.ema.europa.eu/en/medicines/human/EPAR/deltyba (accessed on 15 November 2019).
  23. Ryan, N.J.; Lo, J.H. Delamanid: First global approval. Drugs 2014, 74, 1041–1045.
  24. US FDA. FDA Approves New Drug for Treatment-Resistant Forms of Tuberculosis That Affects the Lungs; US FDA: Silver Spring, MD, USA, 2019.
  25. Baptista, R.; Fazakerley, D.M.; Beckmann, M.; Baillie, L.; Mur, L.A.J. Untargeted metabolomics reveals a new mode of action of pretomanid (PA-824). Sci. Rep. 2018, 8, 5084.
  26. Thompson, A.M.; Bonnet, M.; Lee, H.H.; Franzblau, S.G.; Wan, B.; Wong, G.S.; Cooper, C.B.; Denny, W.A. Antitubercular Nitroimidazoles Revisited: Synthesis and Activity of the Authentic 3-Nitro Isomer of Pretomanid. ACS Med. Chem. Lett. 2017, 8, 1275–1280.
  27. Manjunatha, U.; Boshoff, H.I.; Barry, C.E. The mechanism of action of PA-824: Novel insights from transcriptional profiling. Commun. Integr. Biol. 2009, 2, 215–218.
  28. Reymond, J.-L.; van Deursen, R.; Blum, L.C.; Ruddigkeit, L. Chemical space as a source for new drugs. MedChemComm 2010, 1, 30–38.
  29. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235–242.
  30. Ekins, S.; Freundlich, J.S.; Choi, I.; Sarker, M.; Talcott, C. Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery. Trends Microbiol. 2011, 19, 65–74.
  31. Macalino, S.J.; Gosu, V.; Hong, S.; Choi, S. Role of computer-aided drug design in modern drug discovery. Arch. Pharm. Res. 2015, 38, 1686–1701.
  32. Schwede, T.; Kopp, J.; Guex, N.; Peitsch, M.C. SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Res. 2003, 31, 3381–3385.
  33. Webb, B.; Sali, A. Comparative Protein Structure Modeling Using MODELLER. Curr. Protoc. Protein Sci. 2016, 86, 1–37.
  34. Kim, D.E.; Chivian, D.; Baker, D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 2004, 32, W526–W531.
  35. Schrödinger. Prime. Available online: https://www.schrodinger.com/prime (accessed on 26 October 2019).
  36. Zheng, W.; Zhang, C.; Bell, E.W.; Zhang, Y. I-TASSER gateway: A protein structure and function prediction server powered by XSEDE. Future Gener. Comput. Syst. 2019, 99, 73–85.
  37. Zhang, Y. I-TASSER server for protein 3D structure prediction. BMC Bioinf. 2008, 9, 40.
  38. Yang, J.; Zhang, Y. I-TASSER server: New development for protein structure and function predictions. Nucleic Acids Res. 2015, 43, W174–W181.
  39. Yang, J.; Yan, R.; Roy, A.; Xu, D.; Poisson, J.; Zhang, Y. The I-TASSER Suite: Protein structure and function prediction. Nat. Methods 2015, 12, 7–8.
  40. Roy, A.; Kucukural, A.; Zhang, Y. I-TASSER: A unified platform for automated protein structure and function prediction. Nat. Protoc. 2010, 5, 725–738.
  41. Zhang, Y. I-TASSER: Fully automated protein structure prediction in CASP8. Proteins 2009, 77, 100–113.
  42. Zimmermann, L.; Stephens, A.; Nam, S.Z.; Rau, D.; Kubler, J.; Lozajic, M.; Gabler, F.; Soding, J.; Lupas, A.N.; Alva, V. A Completely Reimplemented MPI Bioinformatics Toolkit with a New HHpred Server at its Core. J. Mol. Biol. 2018, 430, 2237–2243.
  43. Hildebrand, A.; Remmert, M.; Biegert, A.; Soding, J. Fast and accurate automatic structure prediction with HHpred. Proteins 2009, 77, 128–132.
  44. Soding, J.; Biegert, A.; Lupas, A.N. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 2005, 33, W244–W248.
  45. Laskowski, R.A.; MacArthur, M.W.; Moss, D.S.; Thornton, J.M. PROCHECK: A program to check the stereochemical quality of protein structures. J. Appl. Cryst. 1993, 26, 283–291.
  46. Wiederstein, M.; Sippl, M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007, 35, W407–W410.
  47. Eisenberg, D.; Luthy, R.; Bowie, J.U. VERIFY3D: Assessment of protein models with three-dimensional profiles. Methods Enzymol. 1997, 277, 396–404.
  48. Colovos, C.; Yeates, T.O. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci. 1993, 2, 1511–1519.
  49. Hussein, H.A.; Borrel, A.; Geneix, C.; Petitjean, M.; Regad, L.; Camproux, A.C. PockDrug-Server: A new web server for predicting pocket druggability on holo and apo proteins. Nucleic Acids Res. 2015, 43, W436–W442.
  50. Volkamer, A.; Kuhn, D.; Rippmann, F.; Rarey, M. DoGSiteScorer: A web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics 2012, 28, 2074–2075.
  51. Schmidtke, P.; Le Guilloux, V.; Maupetit, J.; Tuffery, P. fpocket: Online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res. 2010, 38, W582–W589.
  52. Le Guilloux, V.; Schmidtke, P.; Tuffery, P. Fpocket: An open source platform for ligand pocket detection. BMC Bioinf. 2009, 10, 168.
  53. Tian, W.; Chen, C.; Lei, X.; Zhao, J.; Liang, J. CASTp 3.0: Computed atlas of surface topography of proteins. Nucleic Acids Res. 2018, 46, W363–W367.
  54. Binkowski, T.A.; Naghibzadeh, S.; Liang, J. CASTp: Computed Atlas of Surface Topography of proteins. Nucleic Acids Res. 2003, 31, 3352–3355.
  55. Dundas, J.; Ouyang, Z.; Tseng, J.; Binkowski, A.; Turpaz, Y.; Liang, J. CASTp: Computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res. 2006, 34, W116–W118.
  56. Koes, D.R.; Camacho, C.J. PocketQuery: Protein-protein interaction inhibitor starting points from protein-protein interaction structure. Nucleic Acids Res. 2012, 40, W387–W392.
  57. Brady, G.P., Jr.; Stouten, P.F. Fast prediction and visualization of protein binding pockets with PASS. J. Comput. Aided Mol. Des. 2000, 14, 383–401.
  58. Halgren, T.A. Identifying and characterizing binding sites and assessing druggability. J. Chem. Inf. Model. 2009, 49, 377–389.
  59. Capra, J.A.; Laskowski, R.A.; Thornton, J.M.; Singh, M.; Funkhouser, T.A. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput. Biol. 2009, 5, e1000585.
  60. Jendele, L.; Krivak, R.; Skoda, P.; Novotny, M.; Hoksza, D. PrankWeb: A web server for ligand binding site prediction and visualization. Nucleic Acids Res. 2019, 47, W345–W349.
  61. Laskowski, R.A.; Watson, J.D.; Thornton, J.M. ProFunc: A server for predicting protein function from 3D structure. Nucleic Acids Res. 2005, 33, W89–W93.
  62. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009, 30, 2785–2791.
  63. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010, 31, 455–461.
  64. Allen, W.J.; Balius, T.E.; Mukherjee, S.; Brozell, S.R.; Moustakas, D.T.; Lang, P.T.; Case, D.A.; Kuntz, I.D.; Rizzo, R.C. DOCK 6: Impact of new features and current docking performance. J. Comput. Chem. 2015, 36, 1132–1156.
  65. Verdonk, M.L.; Cole, J.C.; Hartshorn, M.J.; Murray, C.W.; Taylor, R.D. Improved protein–ligand docking using GOLD. Proteins 2003, 52, 609–623.
  66. Schrödinger. Glide. Available online: https://www.schrodinger.com/glide (accessed on 26 October 2019).
  67. Schrödinger. Induced Fit. Available online: https://www.schrodinger.com/induced-fit (accessed on 26 October 2019).
  68. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 1996, 261, 470–489.
  69. Davis, I.W.; Baker, D. RosettaLigand docking with full ligand and receptor flexibility. J. Mol. Biol. 2009, 385, 381–392.
  70. Wu, G.; Robertson, D.H.; Brooks, C.L., 3rd; Vieth, M. Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm. J. Comput. Chem. 2003, 24, 1549–1562.
  71. Bitencourt-Ferreira, G.; de Azevedo, W.F., Jr. Docking with SwissDock. Methods Mol. Biol. 2019, 2053, 189–202.
  72. Grosdidier, A.; Zoete, V.; Michielin, O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res. 2011, 39, W270–W277.
  73. Koes, D.R.; Camacho, C.J. Pharmer: Efficient and exact pharmacophore search. J. Chem. Inf. Model. 2011, 51, 1307–1314.
  74. Dassault Systèmes BIOVIA. Catalyst. Available online: https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/pharmacophore-and-ligand-based-design.html (accessed on 26 October 2019).
  75. Schneidman-Duhovny, D.; Dror, O.; Inbar, Y.; Nussinov, R.; Wolfson, H.J. PharmaGist: A webserver for ligand-based pharmacophore detection. Nucleic Acids Res. 2008, 36, W223–W228.
  76. Inte:Ligand. LigandScout. Available online: http://www.inteligand.com/ligandscout/ (accessed on 26 October 2019).
  77. Zoete, V.; Daina, A.; Bovigny, C.; Michielin, O. SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening. J. Chem. Inf. Model. 2016, 56, 1399–1404.
  78. Douguet, D. e-LEA3D: A computational-aided drug design web server. Nucleic Acids Res. 2010, 38, W615–W621.
  79. Dallakyan, S.; Olson, A.J. Small-molecule library screening by docking with PyRx. Methods Mol. Biol. 2015, 1263, 243–250.
  80. Schrödinger. PHASE. Available online: https://www.schrodinger.com/phase (accessed on 26 October 2019).
  81. Case, D.A.; Cheatham, T.E., 3rd; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem. 2005, 26, 1668–1688.
  82. Salomon-Ferrer, R.; Case, D.A.; Walker, R.C. An overview of the Amber biomolecular simulation package. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2013, 3, 198–210.
  83. Brooks, B.R.; Brooks, C.L., 3rd; Mackerell, A.D., Jr.; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; et al. CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545–1614.
  84. Miller, B.T.; Singh, R.P.; Klauda, J.B.; Hodoscek, M.; Brooks, B.R.; Woodcock, H.L., 3rd. CHARMMing: A new, flexible web portal for CHARMM. J. Chem. Inf. Model. 2008, 48, 1920–1929.
  85. Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A.E.; Berendsen, H.J. GROMACS: Fast, flexible, and free. J. Comput. Chem. 2005, 26, 1701–1718.
  86. Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19–25.
  87. Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kale, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781–1802.
  88. Schrödinger. Desmond. Available online: https://www.schrodinger.com/desmond (accessed on 26 October 2019).
  89. Zoete, V.; Cuendet, M.A.; Grosdidier, A.; Michielin, O. SwissParam: A fast force field generation tool for small organic molecules. J. Comput. Chem. 2011, 32, 2359–2368.
  90. Jo, S.; Kim, T.; Iyer, V.G.; Im, W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem. 2008, 29, 1859–1865.
  91. Vanommeslaeghe, K.; Hatcher, E.; Acharya, C.; Kundu, S.; Zhong, S.; Shim, J.; Darian, E.; Guvench, O.; Lopes, P.; Vorobyov, I.; et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 2010, 31, 671–690.
  92. Vanommeslaeghe, K.; MacKerell, A.D., Jr. Automation of the CHARMM General Force Field (CGenFF) I: Bond perception and atom typing. J. Chem. Inf. Model. 2012, 52, 3144–3154.
  93. Vanommeslaeghe, K.; Raman, E.P.; MacKerell, A.D., Jr. Automation of the CHARMM General Force Field (CGenFF) II: Assignment of bonded parameters and partial atomic charges. J. Chem. Inf. Model. 2012, 52, 3155–3168.
  94. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 1996, 14, 27–38.
  95. Helguera, A.M.; Combes, R.D.; Gonzalez, M.P.; Cordeiro, M.N. Applications of 2D descriptors in drug design: A DRAGON tale. Curr. Top. Med. Chem. 2008, 8, 1628–1655.
  96. Tetko, I.V.; Gasteiger, J.; Todeschini, R.; Mauri, A.; Livingstone, D.; Ertl, P.; Palyulin, V.A.; Radchenko, E.V.; Zefirov, N.S.; Makarenko, A.S.; et al. Virtual computational chemistry laboratory—Design and description. J. Comput. Aided Mol. Des. 2005, 19, 453–463.
  97. Schrödinger. Canvas. Available online: https://www.schrodinger.com/canvas (accessed on 26 October 2019).
  98. Landrum, G. RDKit: Open-Source Cheminformatics. Available online: http://www.rdkit.org (accessed on 26 October 2019).
  99. Masand, V.H.; Rastija, V. PyDescriptor: A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors. Chemom. Intell. Lab. Syst. 2017, 169, 12–18.
  100. Moriwaki, H.; Tian, Y.S.; Kawashita, N.; Takagi, T. Mordred: A molecular descriptor calculator. J. Cheminform. 2018, 10, 4.
  101. Tosco, P.; Balle, T. Open3DQSAR: A new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J. Mol. Model. 2011, 17, 201–208.
  102. Dong, J.; Yao, Z.J.; Zhu, M.F.; Wang, N.N.; Lu, B.; Chen, A.F.; Lu, A.P.; Miao, H.; Zeng, W.B.; Cao, D.S. ChemSAR: An online pipelining platform for molecular SAR modeling. J. Cheminform. 2017, 9, 27.
  103. BioSolveIT. SeeSAR version 9.2. Available online: https://www.biosolveit.de/SeeSAR/ (accessed on 26 October 2019).
  104. Schrödinger. QikProp. Available online: https://www.schrodinger.com/qikprop (accessed on 26 October 2019).
  105. SimulationsPlus. ADMET Predictor. Available online: https://www.simulations-plus.com/software/admetpredictor/ (accessed on 26 October 2019).
  106. ACD/Labs. Percepta Platform. Available online: https://www.acdlabs.com/products/percepta/ (accessed on 26 October 2019).
  107. Miteva, M.A.; Violas, S.; Montes, M.; Gomez, D.; Tuffery, P.; Villoutreix, B.O. FAF-Drugs: Free ADME/tox filtering of compound collections. Nucleic Acids Res. 2006, 34, W738–W744.
  108. Rasolohery, I.; Moroy, G.; Guyon, F. PatchSearch: A Fast Computational Method for Off-Target Detection. J. Chem. Inf. Model. 2017, 57, 769–777.
  109. Dassault Systèmes BIOVIA. DS TOPKAT. Available online: https://www.3dsbiovia.com/products/collaborative-science/biovia-discovery-studio/qsar-admet-and-predictive-toxicology.html (accessed on 26 October 2019).
  110. Dassault Systèmes BIOVIA. DS ADMET. Available online: https://www.3dsbiovia.com/products/collaborative-science/biovia-pipeline-pilot/component-collections/adme-tox.html (accessed on 26 October 2019).
  111. Poroikov, V.; Filimonov, D.; Lagunin, A.; Gloriozova, T.; Zakharov, A. PASS: Identification of probable targets and mechanisms of toxicity. SAR QSAR Environ. Res. 2007, 18, 101–110.
  112. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep. 2017, 7, 42717.
  113. Cruciani, G.; Carosati, E.; De Boeck, B.; Ethirajulu, K.; Mackie, C.; Howe, T.; Vianello, R. MetaSite: Understanding metabolism in human cytochromes from the perspective of the chemist. J. Med. Chem. 2005, 48, 6970–6979.
  114. Tcheremenskaia, O.; Benigni, R.; Nikolova, I.; Jeliazkova, N.; Escher, S.E.; Batke, M.; Baier, T.; Poroikov, V.; Lagunin, A.; Rautenberg, M.; et al. OpenTox predictive toxicology framework: Toxicological ontology and semantic media wiki-based OpenToxipedia. J. Biomed. Semant. 2012, 3, S7.
  115. Smiesko, M.; Vedani, A. VirtualToxLab: Exploring the Toxic Potential of Rejuvenating Substances Found in Traditional Medicines. Methods Mol. Biol. 2016, 1425, 121–137.
  116. Vedani, A.; Dobler, M.; Smiesko, M. VirtualToxLab—A platform for estimating the toxic potential of drugs, chemicals and natural products. Toxicol. Appl. Pharmacol. 2012, 261, 142–153.
  117. Vedani, A.; Smiesko, M.; Spreafico, M.; Peristera, O.; Dobler, M. VirtualToxLab—In silico prediction of the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. Two years and 2000 compounds of experience: A progress report. ALTEX 2009, 26, 167–176.
  118. Vedani, A.; Dobler, M.; Spreafico, M.; Peristera, O.; Smiesko, M. VirtualToxLab—In silico prediction of the toxic potential of drugs and environmental chemicals: Evaluation status and internet access protocol. ALTEX 2007, 24, 153–161.
  119. Cheng, F.; Li, W.; Zhou, Y.; Shen, J.; Wu, Z.; Liu, G.; Lee, P.W.; Tang, Y. Correction to “admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties”. J. Chem. Inf. Model. 2019.
  120. Yang, H.; Lou, C.; Sun, L.; Li, J.; Cai, Y.; Wang, Z.; Li, W.; Liu, G.; Tang, Y. admetSAR 2.0: Web-service for prediction and optimization of chemical ADMET properties. Bioinformatics 2019, 35, 1067–1069.
  121. Cheng, F.; Li, W.; Zhou, Y.; Shen, J.; Wu, Z.; Liu, G.; Lee, P.W.; Tang, Y. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model. 2012, 52, 3099–3105.
  122. Rudik, A.; Bezhentsev, V.; Dmitriev, A.; Lagunin, A.; Filimonov, D.; Poroikov, V. Metatox-Web application for generation of metabolic pathways and toxicity estimation. J. Bioinform. Comput. Biol. 2019, 17, 1940001.
  123. Rudik, A.V.; Bezhentsev, V.M.; Dmitriev, A.V.; Druzhilovskiy, D.S.; Lagunin, A.A.; Filimonov, D.A.; Poroikov, V.V. MetaTox: Web Application for Predicting Structure and Toxicity of Xenobiotics’ Metabolites. J. Chem. Inf. Model. 2017, 57, 638–642.
  124. Villoutreix, B.O.; Eudes, R.; Miteva, M.A. Structure-based virtual ligand screening: Recent success stories. Comb. Chem. High Throughput Screen. 2009, 12, 1000–1016.
  125. Talele, T.T.; Khedkar, S.A.; Rigby, A.C. Successful applications of computer aided drug discovery: Moving drugs from concept to the clinic. Curr. Top. Med. Chem. 2010, 10, 127–141.
  126. Clark, D.E. What has virtual screening ever done for drug discovery? Expert Opin. Drug Discov. 2008, 3, 841–851.
  127. Scior, T.; Bender, A.; Tresadern, G.; Medina-Franco, J.L.; Martinez-Mayorga, K.; Langer, T.; Cuanalo-Contreras, K.; Agrafiotis, D.K. Recognizing pitfalls in virtual screening: A critical review. J. Chem. Inf. Model. 2012, 52, 867–881.
  128. Baig, M.H.; Ahmad, K.; Roy, S.; Ashraf, J.M.; Adil, M.; Siddiqui, M.H.; Khan, S.; Kamal, M.A.; Provaznik, I.; Choi, I. Computer Aided Drug Design: Success and Limitations. Curr. Pharm. Des. 2016, 22, 572–581.
  129. Coupez, B.; Lewis, R.A. Docking and scoring—Theoretically easy, practically impossible? Curr. Med. Chem. 2006, 13, 2995–3003.
  130. Geppert, H.; Vogt, M.; Bajorath, J. Current trends in ligand-based virtual screening: Molecular representations, data mining methods, new application areas, and performance evaluation. J. Chem. Inf. Model. 2010, 50, 205–216.
  131. Jain, A.N.; Nicholls, A. Recommendations for evaluation of computational methods. J. Comput. Aided Mol. Des. 2008, 22, 133–139.
  132. Lindorff-Larsen, K.; Maragakis, P.; Piana, S.; Shaw, D.E. Picosecond to Millisecond Structural Dynamics in Human Ubiquitin. J. Phys. Chem. B 2016, 120, 8313–8320.
  133. Noe, F. Beating the millisecond barrier in molecular dynamics simulations. Biophys. J. 2015, 108, 228–229.
  134. Shi, J.; Nobrega, R.P.; Schwantes, C.; Kathuria, S.V.; Bilsel, O.; Matthews, C.R.; Lane, T.J.; Pande, V.S. Atomistic structural ensemble refinement reveals non-native structure stabilizes a sub-millisecond folding intermediate of CheY. Sci. Rep. 2017, 7, 44116.
  135. Fujita, T. Recent Success Stories Leading to Commercializable Bioactive Compounds with the Aid of Traditional QSAR Procedures. QSAR 1997, 16, 107–112.
  136. Gao, Q.; Yang, L.; Zhu, Y. Pharmacophore based drug design approach as a practical process in drug discovery. Curr. Comput. Aided Drug Des. 2010, 6, 37–49.
  137. Sardari, S.; Dezfulian, M. Cheminformatics in anti-infective agents discovery. Mini Rev. Med. Chem. 2007, 7, 181–189.
  138. Bostrom, J.; Norrby, P.O.; Liljefors, T. Conformational energy penalties of protein-bound ligands. J. Comput. Aided Mol. Des. 1998, 12, 383–396.
  139. Perola, E.; Charifson, P.S. Conformational analysis of drug-like molecules bound to proteins: An extensive study of ligand reorganization upon binding. J. Med. Chem. 2004, 47, 2499–2510.
  140. Verma, J.; Khedkar, V.M.; Coutinho, E.C. 3D-QSAR in drug design—A review. Curr. Top. Med. Chem. 2010, 10, 95–115.
  141. Hu, Y.; Bajorath, J. Extending the activity cliff concept: Structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database. J. Chem. Inf. Model. 2012, 52, 1806–1811.
  142. Hu, Y.; Stumpfe, D.; Bajorath, J. Advancing the activity cliff concept. F1000Res 2013, 2, 199.
  143. Stumpfe, D.; de la Vega de Leon, A.; Dimova, D.; Bajorath, J. Advancing the activity cliff concept, part II. F1000Res 2014, 3, 75.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Infectious Diseases
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Stephani Joy Macalino
View Times: 564
Revisions: 2 times (View History)
Update Date: 29 Dec 2021
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback