Computational Approaches in Drug Discovery and Design

Pathak, Rajesh Kumar; Singh, Dev Bukhsh; Sagar, Mamta; Baunthiyal, Mamta; Kumar, Anil

doi:10.1007/978-981-15-6815-2_1

Cited by 13 publications

(8 citation statements)

References 69 publications

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“…These interactions can also be utilized in the investigation of antiviral compounds. Furthermore, an additional advantage of these strategies is safety due to natural or plant-based origin of the compounds ( 54 ). Our findings support this strategy and indicate that the selected compounds have a potential to act as antiviral lead compounds against PRRSV.…”

Section: Discussionmentioning

confidence: 99%

Investigating Multi-Target Antiviral Compounds by Screening of Phytochemicals From Neem (Azadirachta indica) Against PRRSV: A Vetinformatics Approach

et al. 2022

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Porcine reproductive and respiratory syndrome virus (PRRSV) is a global health problem for pigs. PRRSV is highly destructive and responsible for significant losses to the swine industry. Vaccines are available but incapable of providing adequate and long-term protection. As a result, effective and safe strategies are urgently needed to combat the virus. The scavenger receptor cysteine-rich domain 5 (SRCR5) in porcine CD163, non-structural protein 4 (Nsp4), and Nsp10 are known to play significant roles in PRRSV infection and disease development. Therefore, we targeted these proteins to identify multi-target antiviral compounds. To identify potent inhibitors, molecular docking of neem phytochemicals was conducted; three compounds [7-deacetyl-7-oxogedunin (CID:1886), Kulactone (CID:15560423), and Nimocin (CASID:104522-76-1)] were selected based on the lowest binding energy and multi-target inhibitory nature. The efficacy and safety of the selected compounds were revealed through the pharmacokinetics analysis and toxicity assessment. Moreover, 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability and dynamic behavior of target proteins and their docked complexes with selected compounds. Besides, molecular mechanics Poisson–Boltzmann surface area method was used to estimate the binding free energy of each protein-ligand complex obtained from the MD simulations and validate the affinities of selected compounds to target proteins. Based on our analysis, we concluded that the identified multi-target compounds can be utilized as lead compounds for the development of natural drugs against PRRSV. If further validated in clinical studies, these compounds can be used individually or in combination against the virus.

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Section: Discussionmentioning

confidence: 99%

Investigating Multi-Target Antiviral Compounds by Screening of Phytochemicals From Neem (Azadirachta indica) Against PRRSV: A Vetinformatics Approach

et al. 2022

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“…In past years, the identification of lead compounds for drug development take much time and cost as well as required good infrastructure experimental facilities [ 11 , 54 ]. Due to advances in structural biology, computer science, and bioinformatics, it becomes easy to find out putative molecule(s) by a screening of a big database that has a strong affinity with the target for experimental evaluation [ 24 , 55 ]. It saves the cost and time of the scientific community.…”

Section: Discussionmentioning

confidence: 99%

“…It saves the cost and time of the scientific community. Most of the medicines available in the market are from a natural source or it is a derivative of naturally occurring molecules [ 11 , 24 ]. Natural compounds have immense potential to inhibit virus and pathogenic proteins and act as antiviral drugs [ 56 – 58 ].…”

Section: Discussionmentioning

confidence: 99%

“…Virtual screening is a computational method widely used for the identification of lead molecules by docking large numbers of compounds with a molecular target of interest to allow evaluation of the binding free energy of the docked/screened compounds using drug discovery programs [ 24 ]. AutoDock vina is a molecular docking and virtual screening program that determines the preferred relative orientation of a ligand during docking or interaction with a molecular target and provides a stable protein–ligand complex structure that exhibits a minimum binding energy [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

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Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies

2022

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Background Porcine reproductive and respiratory syndrome respiratory sickness in weaned and growing pigs, as well as sow reproductive failure, and its infection is regarded as one of the most serious swine illnesses worldwide. Given the current lack of an effective treatment, in this study, we identified natural compounds capable of inhibiting non-structural protein 4 (Nsp4) of the virus, which is involved in their replication and pathogenesis. Results We screened natural compounds (n = 97,999) obtained from the ZINC database against Nsp4 and selected the top 10 compounds for analysing protein–ligand interactions and physicochemical properties. The five compounds demonstrating strong binding affinity were then subjected to molecular dynamics simulations (100 ns) and binding free energy calculations. Based on analysis, we identified four possible lead compounds that represent potentially effective drug-like inhibitors. Conclusions These methods identified that these natural compounds are capable of inhibiting Nsp4 and possibly effective as antiviral therapeutics against PRRSV.

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“…Modern drug development and discovery-related analysis are based on various tools/ software designed using ML approaches. 3D structure and binding sites information are important in choosing the region for which a ligand searching or designing may be started (Pathak et al, 2020). Ligand optimization is required to balance the physicochemical properties and pharmacokinetics of a ligand so that it can qualify for the criteria of the candidate drug.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches and their applications in drug discovery and design

et al. 2022

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This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug‐drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non‐linear datasets, as well as big data of increasing depth and complexity. Various machine learning‐based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand‐based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

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Computational Approaches in Drug Discovery and Design

Cited by 13 publications

References 69 publications

Investigating Multi-Target Antiviral Compounds by Screening of Phytochemicals From Neem (Azadirachta indica) Against PRRSV: A Vetinformatics Approach

Investigating Multi-Target Antiviral Compounds by Screening of Phytochemicals From Neem (Azadirachta indica) Against PRRSV: A Vetinformatics Approach

Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies

Machine learning approaches and their applications in drug discovery and design

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