Excavation of Time sliced and Cost based KDD for the lead generation and promotion on B2C/B2B Sales

Author(s):  
Jency Rena NM ◽  
Gayathry S Warrier ◽  
M Arshey
Keyword(s):  
2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.


2020 ◽  
Vol 23 (17) ◽  
Author(s):  
Aryik Gupta ◽  
Nayana Nimkar

2019 ◽  
Vol 16 (11) ◽  
pp. 898-905
Author(s):  
Harun Patel ◽  
Rahul Pawara ◽  
Sanjay Surana

Quinazoline is the six-membered heterocyclic ring system reported for its versatile biological activities. This characteristic feature of quinazoline makes it a good template for a lead generation library. Ring opening is one of the major concerns in the synthesis of quinazolin-4(3H)-one that results in diamide formation. Here, alternative fusion strategy is reported, which is a time-saving and costeffective method to overcome the ring opening problem associated with the synthesis of benzo[ d][1,3]oxazin-4-one and quinazolin-4(3H)-one.


Author(s):  
Trupti. S. Chitre ◽  
Kalyani. D. Asgaonkar ◽  
Amrut B. Vikhe ◽  
Shital M Patil ◽  
Dinesh. R. Garud ◽  
...  

Background: Diarylquinolines like Bedaquiline have shown promising antitubercular activity by their action of Mycobacterial ATPase. Objective: The structural features necessary for good antitubercular activity for a series of quinoline derivatives were explored through computational chemistry tools like QSAR and combinatorial library generation. In the current study, 3-Chloro-4-(2-mercaptoquinoline-3-yl)-1-substitutedphenylazitidin-2-one derivatives have been designed and synthesized based on molecular modeling studies as anti-tubercular agents. Method: 2D and 3DQSAR analysis was used to designed compounds having quinoline scaffold. The synthesized compounds were evaluated against active and dormant strains of Mycobacterium tuberculosis (MTB) H37 Ra and Mycobacterium bovis BCG. The compounds were also tested for cytotoxicity against MCF-7, A549 and Panc-1 cell lines using MTT assay. Binding affinity of designed compounds was gauged by molecular docking studies. Results: Statistically significant QSAR models generated by SA-MLR method for 2D QSAR exhibited r2 = 0.852, q2 = 0.811and whereas 3D QSAR with SA-kNN showed q2 = 0.77. The synthesized compounds exhibited MIC in the range of 1.38-14.59(µg/ml) .These compounds showed some crucial interaction with MTB Atpase. Conclusion: The present study has shown some promising results which can be further explored for lead generation.


2020 ◽  
Author(s):  
Ben Geoffrey A S ◽  
Akhil Sanker ◽  
Host Antony Davidd ◽  
Judith Gracia

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>


2018 ◽  
Vol 126 (8) ◽  
pp. 595-601 ◽  
Author(s):  
Hiroyuki INANO ◽  
Keiichi TOMITA ◽  
Tatsumi TADA ◽  
Naoki HIROYOSHI

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