In silico approaches for inhibitor designing against Plasmepsin-II of malarial parasite, Plasmodium malariae

Kashyap, Manila; Sohpal, Vipan Kumar; Mahajan, Parul

doi:10.21786/bbrc/19.1/5

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Monika Samant et al [10] has developed a protein-protein interaction network of Plasmodium falciparum and human host by integrating experimental data and computational prediction of interactions using the interolog method. Manila et al [11] has also studied inhibitor against the plasmepsin II. This is an aspartic protease encoded by the malarial parasite that is essential for host haemoglobin degradation.…”

Section: Drug Design For Malaria Using Deep Learning and Others In Silico Techniquesmentioning

confidence: 99%

“…DeepMalaria (a graph based model using SMILE) [9] Analysis of malaria inhibitors [10] Prediction of antimalarial drug target [12] inhibitor designing against malarial parasite [11] Note: These are the most significant paper for in-silico malaria research and DeepMalaria is the only paper where authors has used deep neural network for malaria drug design.…”

Section: Description Referencesmentioning

confidence: 99%

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Drug Design for Malaria with Artificial Intelligence (AI)

Ghosh¹,

Choudhuri²

2021

Plasmodium Species and Drug Resistance

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Malaria is a deadly disease caused by the plasmodium parasites. Approximately 210 million people get affected by malaria every year resulting in half a million deaths. Among several species of the parasite, Plasmodium falciparum is the primary cause of severe infection and death. Several drugs are available for malaria treatment in the market but plasmodium parasites have successfully developed resistance against many drugs over the years. This poses a serious threat to efficacy of the treatments and continuing discovery of new drug is necessary to tackle the situation, especially due to failure in designing an effective vaccine. People are now trying to design new drugs for malaria using AI technologies which can substantially reduce the time and cost required in classical drug discovery programs. In this chapter, we provide a comprehensive overview of a road map for several AI based computational techniques which can be implemented in a malaria drugs discovery program. Classical computers has limiting computing power. So, researchers are also trying to harness quantum machine learning to speed up the drug discovery processes.

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Section: Drug Design For Malaria Using Deep Learning and Others In Silico Techniquesmentioning

confidence: 99%

Section: Description Referencesmentioning

confidence: 99%

Drug Design for Malaria with Artificial Intelligence (AI)

Ghosh¹,

Choudhuri²

2021

Plasmodium Species and Drug Resistance

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In Silico Docking, ADMET and QSAR Study of few Antimalarial Phytoconstituents as Inhibitors of Plasmepsin II of P. falciparum Against Malaria

Sabiha

Chetia

Kardong

2020

CDTH

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Background: Malaria is endemic in various parts of India particularly in the North-Eastern states with Plasmodium falciparum the most prevalent human malaria parasite. Plant-derived compounds have always received tremendous importance in the area of drug discovery and development and scientific study of traditional medicinal plants are of great importance to mankind. Objective: The present work deals with the computational study of some antimalarial compounds obtained from few medicinal plants used by the tribal inhabitants of the North-Eastern region of India for treating malaria. Methods: In silico methodologies were performed to study the ligand-receptor interactions. Target was identified based on the pharmacophore mapping approach. A total of 18 plant-derived compounds were investigated in order to estimate the binding energies of the compounds with their drug target through molecular docking using Autodock 4.2. ADMET filtering for determining the pharmacokinetic properties of the compounds were done using Mobyle@RPBS server. Subsequent Quantitative-Structure Activity Relationship analysis for bioactivity prediction (IC50) of the compounds was done using Easy QSAR 1.0. Results: The docking result identified Salannin to be the most potent Plasmepsin II inhibitor while the QSAR analysis identified Lupeol to have the least IC50 value. Most of the compounds have passed the ADME/Tox filteration. Conclusion: Salannin and Lupeol were found to be the most potent antimalarial compounds that can act as successful inhibitors against Plasmepsin II of P. falciparum. The compounds Salannin and Lupeol are found in Azadirachta indica and Swertia chirata plants respectively, abundantly available in the North-Eastern region of India and used by many inhabiting tribes for treatment of malaria and its symptoms.

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In silico approaches for inhibitor designing against Plasmepsin-II of malarial parasite, Plasmodium malariae

Cited by 2 publications

References 19 publications

Drug Design for Malaria with Artificial Intelligence (AI)

Drug Design for Malaria with Artificial Intelligence (AI)

In Silico Docking, ADMET and QSAR Study of few Antimalarial Phytoconstituents as Inhibitors of Plasmepsin II of P. falciparum Against Malaria

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