Improved Classification of Blood-Brain-Barrier Drugs Using Deep Learning

Miao, Rui; Xia, Liang-Yong; Chen, Hao-Heng; Huang, Haihui; Liang, Yong

doi:10.1038/s41598-019-44773-4

Cited by 58 publications

(46 citation statements)

References 46 publications

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“…The blood–brain barrier (BBB) protects the central nervous system (CNS) preventing certain substances (mostly harmful) from entering the brain tissues. The BBB limits the passage of most of the external compounds to maintain CNS at a steady state (Durán-Iturbide et al, 2020 ; Miao et al, 2019 ). In addition, P-glycoprotein (P-gp) actively transports a wide variety of compounds out of cells.…”

Section: Resultsmentioning

confidence: 99%

Identification of 1,2,3-triazole-phthalimide derivatives as potential drugs against COVID-19: a virtual screening, docking and molecular dynamic study

Holanda

Lima

Silva

et al. 2021

Journal of Biomolecular Structure and Dynamics

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In this work we aimed to perform an in silico predictive screening, docking and molecular dynamic study to identify 1,2,3-triazole-phthalimide derivatives as drug candidates against SARS-CoV-2. The in silico prediction of pharmacokinetic and toxicological properties of hundred one 1,2,3-triazole-phtalimide derivatives, obtained from SciFinderV R library, were investigated. Compounds that did not show good gastrointestinal absorption, violated the Lipinski's rules, proved to be positive for the AMES test, and showed to be hepatotoxic or immunotoxic in our ADMET analysis, were filtered out of our study. The hit compounds were further subjected to molecular docking on SARS-CoV-2 target proteins. The ADMET analysis revealed that 43 derivatives violated the Lipinski's rules and 51 other compounds showed to be positive for the toxicity test. Seven 1,2,3-triazole-phthalimide derivatives (A7, A8, B05, E35, E38, E39, and E40) were selected for molecular docking and MFCC-ab initio analysis. The results of molecular docking pointed the derivative E40 as a promising compound interacting with multiple target proteins of SARS-CoV-2. The complex E40-M pro was found to have minimum binding energy of À10.26 kcal/mol and a general energy balance, calculated by the quantum mechanical analysis, of À8.63 eV. MD simulation and MMGBSA calculations confirmed that the derivatives E38 and E40 have high binding energies of À63.47 ± 3 and À63.31 ± 7 kcal/mol against SARS-CoV-2 main protease. In addition, the derivative E40 exhibited excellent interaction values and inhibitory potential against SAR-Cov-2 main protease and viral nucleocapsid proteins, suggesting this derivative as a potent antiviral for the treatment and/or prophylaxis of COVID-19.

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Section: Resultsmentioning

confidence: 99%

Identification of 1,2,3-triazole-phthalimide derivatives as potential drugs against COVID-19: a virtual screening, docking and molecular dynamic study

Holanda

Lima

Silva

et al. 2021

Journal of Biomolecular Structure and Dynamics

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“…For example, in brain, machine learning can be used for image analysis [17,18]. Machine learning is poised to impact brain barriers and brain fluids research in, for example, analysis of brain edema on radiographic images [19,20] or potentially to predict brain drug penetration [21,22].…”

Section: Toolsmentioning

confidence: 99%

This was the year that was: brain barriers and brain fluid research in 2019

Keep

Jones²,

Drewes

2020

Fluids Barriers CNS

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This editorial highlights advances in brain barrier and brain fluid research published in 2019, as well as addressing current controversies and pressing needs. Topics include recent advances related to: the cerebral endothelium and the neurovascular unit; the choroid plexus, arachnoid membrane; cerebrospinal fluid and the glymphatic hypothesis; the impact of disease states on brain barriers and brain fluids; drug delivery to the brain; and translation of preclinical data to the clinic. This editorial also mourns the loss of two important figures in the field, Malcolm B. Segal and Edward G. Stopa.

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“…Deep learning is a popular artificial intelligence (AI) way that has been successfully applied in medical diagnoses [ 1 ], cellular image analysis [ 2 ], chemical syntheses [ 3 ], classification of drugs [ 4 ] and so on [ 5 ]. Deep learning is a promising technology for the development and discovery of innovative drugs.…”

Section: Introductionmentioning

confidence: 99%

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Bai

Tan

et al. 2020

Briefings in Bioinformatics

191

127

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Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

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Improved Classification of Blood-Brain-Barrier Drugs Using Deep Learning

Cited by 58 publications

References 46 publications

Identification of 1,2,3-triazole-phthalimide derivatives as potential drugs against COVID-19: a virtual screening, docking and molecular dynamic study

Identification of 1,2,3-triazole-phthalimide derivatives as potential drugs against COVID-19: a virtual screening, docking and molecular dynamic study

This was the year that was: brain barriers and brain fluid research in 2019

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

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