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Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics
| Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics |
| Autore | Tutone Marco |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (387 p.) |
| Soggetto topico |
Research & information: general
Chemistry |
| Soggetto non controllato |
3D-QSAR
pharmacophore modeling ligand-based model HDACs isoform-selective histone deacetylase inhibitors aminophenylbenzamide hERG toxicity drug discovery fingerprints machine learning deep learning gene expression signature drug repositioning approaches RNA expression regulation high-throughput virtual screening dual-target lead discovery neurodegenerative disorders Alzheimer's disease dual mode of action multi-modal nicotinic acetylcholine receptor acetylcholinesterase molecular docking methotrexate drug resistance human dihydrofolate reductase virtual screening molecular dynamics simulation epitope binning epitope mapping epitope prediction antibody:antigen interactions protein docking glycoprotein D (gD) herpes simplex virus fusion proteins Src inhibitors pharmacophore model molecular dynamics simulations in silico COX-2 inhibitors molecular modeling sodium-glucose co-transporters 2 FimH uropathogenic bacteria urinary tract infections diabetes drug-resistance mutations HIV-2 protease structural characterization induced structural deformations SARS-CoV-2 COVID-19 multiprotein inhibiting natural compounds MD simulation 3CL-Pro antivirals docking simulations drug repurposing consensus models binding space isomeric space MRP4 SNPs variants protein threading modeling molecular dynamics binding site hTSPO PK11195 cholesterol homology modeling molecular dynamics (MD) simulation carbon nanotubes Stone-Wales defects haeckelite defects doxorubicin encapsulation drug delivery system binding free energies noncovalent interactions main protease mutants inhibitors PF-00835231 Mycobacterium tuberculosis tuberculosis proteasome natural compounds multiscale multitargeting polypharmacology computational biology drug repositioning structural bioinformatics proteomic signature skin aging oxidative stress aging progression mechanism genome-wide genetic and epigenetic network (GWGEN) systems medicine design multiple-molecule drug immunoproteasome non-covalent inhibitors MD binding metadynamics induced-fit docking |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Computational Approaches |
| Record Nr. | UNINA-9910557519403321 |
Tutone Marco
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Statistical Data Modeling and Machine Learning with Applications
| Statistical Data Modeling and Machine Learning with Applications |
| Autore | Gocheva-Ilieva Snezhana |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (184 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
mathematical competency
assessment machine learning classification and regression tree CART ensembles and bagging ensemble model multivariate adaptive regression splines cross-validation dam inflow prediction long short-term memory wavelet transform input predictor selection hyper-parameter optimization brain-computer interface EEG motor imagery CNN-LSTM architectures real-time motion imagery recognition artificial neural networks banking hedonic prices housing quantile regression data quality citizen science consensus models clustering Gower's interpolation formula Gower's metric mixed data multidimensional scaling classification data-adaptive kernel functions image data multi-category classifier predictive models support vector machine stochastic gradient descent damped Newton convexity METABRIC dataset breast cancer subtyping deep forest multi-omics data categorical data similarity feature selection kernel density estimation non-linear optimization kernel clustering |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557359003321 |
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Gocheva-Ilieva Snezhana
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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