Has Molecular Docking Ever Brought us a Medicine?

Phillips, Mark; Stewart, Marisa A.; Woodling, Darby L.; Xie, Zhong-Ru

doi:10.5772/intechopen.72898

Cited by 39 publications

(30 citation statements)

References 112 publications

(124 reference statements)

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“…Larger fluctuations are seen in the F(BE

) for extreme values in BE

(i.e., for values of BE

below −13.0 and above −4 kcal/mol), which has to be attributed to the reduced number of datapoints available for this range of experimental binding energies. In line with our findings, the molecular docking approach has seen some criticism for its reliability in the literature [ 25 ]. However, we cannot ignore that the molecular docking approach has been successfully used to identify lead-drug-like compounds for various targets previously [ 26 ].…”

Section: Resultssupporting

confidence: 84%

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein–Inhibitor Complexes in Relevance to Developing Parkinson’s Therapeutics

Murugan

Muvva²,

Jeyarajpandian³

et al. 2020

IJMS

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Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as benzylamine, phenethylamine and dopamine with the help of Flavin adenine dinucleotide (FAD) cofactor. The Parkinson’s disease associated symptoms can be treated using inhibitors of MAO-B as the dopamine degradation can be reduced. Currently, many inhibitors are available having micromolar to nanomolar binding affinities. However, still there is demand for compounds with superior binding affinity and binding specificity with favorable pharmacokinetic properties for treating Parkinson’s disease and computational screening methods can be majorly recruited for this. However, the accuracy of currently available force-field methods for ranking the inhibitors or lead drug-like compounds should be improved and novel methods for screening compounds need to be developed. We studied the performance of various force-field-based methods and data driven approaches in ranking about 3753 compounds having activity against the MAO-B target. The binding affinities computed using autodock and autodock-vina are shown to be non-reliable. The force-field-based MM-GBSA also under-performs. However, certain machine learning approaches, in particular KNN, are found to be superior, and we propose KNN as the most reliable approach for ranking the complexes to reasonable accuracy. Furthermore, all the employed machine learning approaches are also computationally less demanding.

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“…Larger fluctuations are seen in the F(BE

) for extreme values in BE

(i.e., for values of BE

Section: Resultssupporting

confidence: 84%

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein–Inhibitor Complexes in Relevance to Developing Parkinson’s Therapeutics

Murugan

Muvva²,

Jeyarajpandian³

et al. 2020

IJMS

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show abstract

“…When information about the three-dimensional structure of the target is known, Structure-Based Drug Design (SBDD) methods are applied for virtual screening in databases of available samples or de novo design of potential ligands with the subsequent synthesis and testing of activity in vitro. Despite the recognized limitations of SBDD methods [3], dozens of newly launched drugs have been developed using this approach [4], which is also utilized to overcome a target's drug resistance [5]. Historically, the first computational methodology applied to research and development of new drugs was Ligand-Based Drug Design (LBDD) [6,7].…”

Section: Introductionmentioning

confidence: 99%

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds

et al. 2019

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Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure–activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.

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“…It is a form of structure-based process that measures the binding affinities between small molecules and macromolecular targets like proteins. Moreover, it is also used to understand the possible molecular mechanism of action of various pharmacological responses [5,42]. From this understanding, this study was performed to comprehend the molecular mechanism of action better and to correlate their findings with the experimental results.…”

Section: Resultsmentioning

confidence: 99%

Comparative Study of Piper sylvaticum Roxb. Leaves and Stems for Anxiolytic and Antioxidant Properties Through In Vivo, In Vitro, and In Silico Approaches

et al. 2020

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Piper sylvaticum Roxb. is traditionally used by the indigenous people of tropical and subtropical countries like Bangladesh, India, and China for relieving the common cold or a variety of chronic diseases, such as asthma, chronic coughing, piles, rheumatic pain, headaches, wounds, tuberculosis, indigestion, and dyspepsia. This study tested anxiolytic and antioxidant activities by in vivo, in vitro, and in silico experiments for the metabolites extracted (methanol) from the leaves and stems of P. sylvaticum (MEPSL and MEPSS). During the anxiolytic evaluation analyzed by elevated plus maze and hole board tests, MEPSL and MEPSS (200 and 400 mg/kg, body weight) exhibited a significant and dose-dependent reduction of anxiety-like behavior in mice. Similarly, mice treated with MEPSL and MEPSS demonstrated dose-dependent increases in locomotion and CNS simulative effects in open field test. In addition, both extracts (MEPSL and MEPSS) also showed moderate antioxidant activities in DPPH scavenging and ferric reducing power assays compared to the standard, ascorbic acid. In parallel, previously isolated bioactive compounds from this plant were documented and subjected to a molecular docking study to correlate them with the pharmacological outcomes. The selected four major phytocompounds displayed favorable binding affinities to potassium channel and xanthine oxidoreductase enzyme targets in molecular docking experiments. Overall, P. sylvaticum is bioactive, as is evident through experimental and computational analysis. Further experiments are necessary to evaluate purified novel compounds for the clinical evaluation.

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Has Molecular Docking Ever Brought us a Medicine?

Cited by 39 publications

References 112 publications

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein–Inhibitor Complexes in Relevance to Developing Parkinson’s Therapeutics

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein–Inhibitor Complexes in Relevance to Developing Parkinson’s Therapeutics

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds

Comparative Study of Piper sylvaticum Roxb. Leaves and Stems for Anxiolytic and Antioxidant Properties Through In Vivo, In Vitro, and In Silico Approaches

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