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Sankalp Jain

Researcher at National Institutes of Health

Publications -  42
Citations -  340

Sankalp Jain is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Virtual screening & Medicine. The author has an hindex of 7, co-authored 37 publications receiving 199 citations. Previous affiliations of Sankalp Jain include Jaypee University of Information Technology & University of Vienna.

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The binding modes and binding affinities of artemisinin derivatives with Plasmodium falciparum Ca2+-ATPase (PfATP6)

TL;DR: The docking, Prime/MM-GBSA and eMBrAcE based prediction model is an efficient tool for generating more potent and specific inhibitors of PfATP6 by testing rationally designed lead compound based on artemisinin derivatization.
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Comparing the performance of meta-classifiers-a case study on selected imbalanced data sets relevant for prediction of liver toxicity.

TL;DR: A comparison of the performance of seven different meta-classifiers for their ability to handle imbalanced datasets, whereby Random Forest is used as base-classifier and Stratified bagging, MetaCost and CostSensitiveClassifier were found to be the best performing among all the methods.
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Outbreak Prediction of COVID-19 for Dense and Populated Countries Using Machine Learning

TL;DR: In this article, the authors developed an outbreak prediction system for COVID-19 for the top 10 highly and densely populated countries using 9 different machine learning algorithms, achieving an average accuracy of 87.9% and a highest accuracy of 99.93% for Ethiopia using Auto-Regressive Moving Average (ARMA).
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Discovery of TMPRSS2 Inhibitors from Virtual Screening as a Potential Treatment of COVID-19

TL;DR: In this article, the authors focused on the human host cell transmembrane protease serine 2 (TMPRSS2), which plays an important role in the viral life cycle by cleaving the spike protein to initiate membrane fusion.
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Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.

TL;DR: In this paper, the authors report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity.