Can an in silico drug-target search method be used to probe potential mechanisms of medicinal plant ingredients?

2003 ◽  
Vol 20 (4) ◽  
pp. 432 ◽  
Author(s):  
Xin Chen ◽  
Choong Yong Ung ◽  
Yuzong Chen
Keyword(s):  
Medicinal Plant ◽  
In Silico ◽  
Drug Target ◽  
Search Method ◽  
Target Search ◽  
Probe Potential

scholarly journals Drug Repurposing for Candidate SARS-CoV-2 Main Protease Inhibitors by a Novel in Silico Method

2020 ◽  
Author(s):  
Milan Sencanski ◽  
Vladimir Perovic ◽  
Snezana Pajovic ◽  
Miroslav Adzic ◽  
Slobodan Paessler ◽  
...  
Keyword(s):  
Protease Inhibitors ◽  
In Silico ◽  
Drug Target ◽  
Transmission Rate ◽  
Experimental Testing ◽  
Drug Repurposing ◽  
Global Public Health ◽  
Main Protease ◽  
Additional Approach ◽  
Drugbank Database

<p>The SARS-CoV-2 outbreak caused an unprecedented global public health threat, having a high transmission rate with currently no drugs or vaccines approved. An alternative powerful additional approach to counteract COVID-19 is <em>in silico</em> drug repurposing. The SARS-CoV-2 main protease is essential for viral replication and an attractive drug target. In this study, we used the virtual screening (VS) protocol with both long-range and short-range interactions to select candidate SARS-CoV-2 main protease inhibitors. First, the ISM applied for Small Molecules was used for searching the Drugbank database and further followed by molecular docking. After <em>in silico</em> screening of drug space, we identified 57 drugs as potential SARS-CoV-2 main protease inhibitors that we propose for further experimental testing.</p>

scholarly journals A Program for Automated Data Mining of PubChem to Screen a Billion Compounds and Generate by Machine Learning Based AutoQSAR Algorithm Anti-Corona Viral Drug Leads (Replicase Polyprotein 1ab Inhibitors) and in Silico Study of the Top Drug Lead Compounds

2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Akhil Sanker ◽  
Host Antony Davidd ◽  
Judith Gracia
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
In Silico ◽  
Drug Target ◽  
Data Set ◽  
Lead Compounds ◽  
Pubchem Database ◽  
Drug Lead ◽  
Lead Generation ◽  
Drug Leads

Our work is composed of a python program for automatic data mining of PubChem database to collect data associated with the corona virus drug target replicase polyprotein 1ab (UniProt identifier : POC6X7 ) of data set involving active compounds, their activity value (IC50) and their chemical/molecular descriptors to run a machine learning based AutoQSAR algorithm on the data set to generate anti-corona viral drug leads. The machine learning based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated each time the program is run is reflective of the constantly growing PubChem database is an important dynamic feature of the program which facilitates fast and dynamic anti-corona viral drug lead generation reflective of the constantly growing PubChem database. The program prints out the top anti-corona viral drug leads after screening PubChem library which is over a billion compounds. The interaction of top drug lead compounds generated by the program and two corona viral drug target proteins, 3-Cystiene like Protease (3CLPro) and Papain like protease (PLpro) was studied and analysed using molecular docking tools. The compounds generated as drug leads by the program showed favourable interaction with the drug target proteins and thus we recommend the program for use in anti-corona viral compound drug lead generation as it helps reduce the complexity of virtual screening and ushers in an age of automatic ease in drug lead generation. The leads generated by the program can further be tested for drug potential through further In Silico, In Vitro and In Vivo testing <div><br></div><div><div>The program is hosted, maintained and supported at the GitHub repository link given below</div><div><br></div><div>https://github.com/bengeof/Drug-Discovery-P0C6X7</div></div><div><br></div>

COMPUTATIONAL APPROACHES FOR DRUG DISCOVERY FROM MEDICINAL PLANTS IN THE ERA OF DATA DRIVEN RESEARCH

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (08) ◽  
pp. 7-23
Author(s):  
Pratibha Pansari ◽  
Keyword(s):  
Drug Discovery ◽  
Medicinal Plants ◽  
In Silico ◽  
Drug Target ◽  
Target Gene ◽  
Scientific Work ◽  
Data Driven ◽  
Network Pharmacology ◽  
Gene Pathway ◽  
Topological Network

The significant scientific work on the development of bio-active compound databases, computational technologies, and the integration of Information Technology with Biotechnology has brought a revolution in the domain of drug discovery. These tools facilitate the medicinal plant-based in silico drug discovery, which has become the frontier of pharmacological science. In this review article, we elucidate the methodology of in silico drug discovery for the medicinal plants and present an outlook on recent tools and technologies. Further, we explore the multi-component, multi-target, and multi-pathway mechanism of the bio-active compounds with the help of Network Pharmacology, which enables us to create a topological network between drug, target, gene, pathway, and disease.

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