Cargando…

Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling

Detalles Bibliográficos
Autores principales:	Cortes-Ciriano, Isidro, van Westen, Gerard JP, Murrell, Daniel S, Lenselink, Eelke B, Bender, Andreas, Malliavin, Therese E
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2015
Materias:	Meeting Abstract
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4340128/ http://dx.doi.org/10.1186/1471-2105-16-S3-A4

Ejemplares similares

Proteochemometric modeling in a Bayesian framework
por: Cortes-Ciriano, Isidro, et al.
Publicado: (2014)

Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling
por: Cortes-Ciriano, Isidro, et al.
Publicado: (2015)

Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules
por: Paricharak, Shardul, et al.
Publicado: (2015)

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets
por: van Westen, Gerard JP, et al.
Publicado: (2013)

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules
por: Murrell, Daniel S, et al.
Publicado: (2015)

Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel
por: Cortés-Ciriano, Isidro, et al.
Publicado: (2016)

Predicting the binding type of compounds on the 4 adenosine receptors using proteochemometric models
por: van den Hoven, Olaf, et al.
Publicado: (2010)

Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling
por: van Westen, Gerard J. P., et al.
Publicado: (2014)

KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
por: Cortés-Ciriano, Isidro, et al.
Publicado: (2019)

Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data
por: Allen, Chad H. G., et al.
Publicado: (2016)

Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets
por: van Westen, Gerard JP, et al.
Publicado: (2013)

Significantly Improved HIV Inhibitor Efficacy Prediction Employing Proteochemometric Models Generated From Antivirogram Data
por: van Westen, Gerard J. P., et al.
Publicado: (2013)

Molecular bioactivity extrapolation to novel targets by support vector machines
por: Van Westen, Gerard JP, et al.
Publicado: (2010)

Which Compound to Select in Lead Optimization? Prospectively Validated Proteochemometric Models Guide Preclinical Development
por: van Westen, Gerard J. P., et al.
Publicado: (2011)

Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors
por: Bongers, Brandon J., et al.
Publicado: (2023)

Identification of Allosteric Modulators of Metabotropic Glutamate 7 Receptor Using Proteochemometric Modeling
por: Tresadern, Gary, et al.
Publicado: (2017)

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data
por: Bender, Andreas, et al.
Publicado: (2021)

Identification of novel small molecule inhibitors for solute carrier SGLT1 using proteochemometric modeling
por: Burggraaff, Lindsey, et al.
Publicado: (2019)

Nongenomic effects of estrogens on signal transduction pathways in estrogen-sensitive carcinoma cell lines
por: Djahansouzi, S, et al.
Publicado: (2001)

Proteochemometric modeling of HIV protease susceptibility
por: Lapins, Maris, et al.
Publicado: (2008)

Unsupervised Representation Learning for Proteochemometric Modeling
por: Kim, Paul T., et al.
Publicado: (2021)

3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors
por: Gorostiola González, Marina, et al.
Publicado: (2023)

Linking the Resource Description Framework to cheminformatics and proteochemometrics
por: Willighagen, Egon L, et al.
Publicado: (2011)

Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis
por: Harigua-Souiai, Emna, et al.
Publicado: (2015)

QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction
por: Cortés-Ciriano, Isidro, et al.
Publicado: (2020)

Screening of selective histone deacetylase inhibitors by proteochemometric modeling
por: Wu, Dingfeng, et al.
Publicado: (2012)

Origin of aromatase inhibitory activity via proteochemometric modeling
por: Simeon, Saw, et al.
Publicado: (2016)

Application of fourier transform and proteochemometrics principles to protein engineering
por: Cadet, Frédéric, et al.
Publicado: (2018)

Proteochemometric Method for pIC50 Prediction of Flaviviridae
por: Singh, Divye, et al.
Publicado: (2022)

Large-Scale Modeling of Sparse Protein Kinase Activity Data
por: Luukkonen, Sohvi, et al.
Publicado: (2023)

Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling
por: Freyhult, Eva, et al.
Publicado: (2005)

Balancing Data on Deep Learning-Based Proteochemometric Activity Classification
por: Lopez-del Rio, Angela, et al.
Publicado: (2021)

Experimental validation of in silico target predictions on synergistic protein targets
por: Cortes-Ciriano, Isidro, et al.
Publicado: (2013)

Utilizing traditional Chinese for the discovery of efficacious new drugs
por: Bender, Andreas
Publicado: (2012)

Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets
por: Cortes-Ciriano, Isidro
Publicado: (2016)

Nocodazole provokes an apoptosis in isopropyl-N-phenyl carbamate resistant and sensitive Nicotiana lines but in two different ways
por: Yemets, Alla I, et al.
Publicado: (2005)

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set
por: Lenselink, Eelke B., et al.
Publicado: (2017)

Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge
por: Lenselink, Eelke B., et al.
Publicado: (2021)

Cell line development using the SEFEX system
por: Greulich, Benedikt, et al.
Publicado: (2011)

A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms
por: Lapins, Maris, et al.
Publicado: (2013)

Cannot write session to /tmp/vufind_sessions/sess_u8j86gjrvhqurmgg92v13cddv9