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Jianlong Peng

Researcher at Chinese Academy of Sciences

Publications -  10
Citations -  515

Jianlong Peng is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Virtual screening & Docking (molecular). The author has an hindex of 9, co-authored 10 publications receiving 386 citations. Previous affiliations of Jianlong Peng include East China University of Science and Technology.

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In silico ADME/T modelling for rational drug design

TL;DR: The development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models are introduced.
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Identifying novel selective non-nucleoside DNA methyltransferase 1 inhibitors through docking-based virtual screening.

TL;DR: A novel DNMT1 inhibitor is identified by combining docking-based virtual screening with biochemical analyses and similarity-based analog searching, and compounds DC_501 and DC_517 were found to be more potent than DC_05, which significantly inhibited cancer cell proliferation.
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In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion.

TL;DR: The promising results suggest that the proposed ligand-based target fishing approach is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities.
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TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds

TL;DR: This work introduces an online implementation of a recently published computational model for target prediction, TarPred, based on a reference library containing 533 individual targets with 179 807 active ligands, and provides the top ranked 30 interacting targets.
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Estimation of acute oral toxicity in rat using local lazy learning.

TL;DR: A consensus model based on the predicted values of individual LLL models of LD50 is developed, yielding correlation coefficients R2 of 0.712 on a test set containing 2,896 compounds.