Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Jonathan Alvarsson"'
Autor:
Staffan Arvidsson McShane, Ulf Norinder, Jonathan Alvarsson, Ernst Ahlberg, Lars Carlsson, Ola Spjuth
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-17 (2024)
Abstract Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complet
Externí odkaz:
https://doaj.org/article/5bc293f6d66346fca78338f8c969d309
Autor:
Laeeq Ahmed, Hiba Alogheli, Staffan Arvidsson McShane, Jonathan Alvarsson, Arvid Berg, Anders Larsson, Wesley Schaal, Erwin Laure, Ola Spjuth
Publikováno v:
Journal of Cheminformatics, Vol 12, Iss 1, Pp 1-11 (2020)
Abstract Background Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues. Contributions We present an open-source, extendable web service
Externí odkaz:
https://doaj.org/article/3cc3e6c22dfd49d4824bba59b083b156
Publikováno v:
Journal of Cheminformatics, Vol 10, Iss 1, Pp 1-10 (2018)
Abstract Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets effic
Externí odkaz:
https://doaj.org/article/d1e4c95911014903976a9ab48d8fbd72
Autor:
Maris Lapins, Staffan Arvidsson, Samuel Lampa, Arvid Berg, Wesley Schaal, Jonathan Alvarsson, Ola Spjuth
Publikováno v:
Journal of Cheminformatics, Vol 10, Iss 1, Pp 1-10 (2018)
Abstract Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water–octanol distribution coefficient (logD) for chemical compounds, aiding drug discover
Externí odkaz:
https://doaj.org/article/1d8de455ff4248958e66beecd6f355bf
Autor:
Egon L. Willighagen, John W. Mayfield, Jonathan Alvarsson, Arvid Berg, Lars Carlsson, Nina Jeliazkova, Stefan Kuhn, Tomáš Pluskal, Miquel Rojas-Chertó, Ola Spjuth, Gilleain Torrance, Chris T. Evelo, Rajarshi Guha, Christoph Steinbeck
Publikováno v:
Journal of Cheminformatics, Vol 9, Iss 1, Pp 1-19 (2017)
Abstract Background The Chemistry Development Kit (CDK) is a widely used open source cheminformatics toolkit, providing data structures to represent chemical concepts along with methods to manipulate such structures and perform computations on them.
Externí odkaz:
https://doaj.org/article/04d9c1e269724925b7979d21a9dd3f92
Autor:
Samuel Lampa, Jonathan Alvarsson, Staffan Arvidsson Mc Shane, Arvid Berg, Ernst Ahlberg, Ola Spjuth
Publikováno v:
Frontiers in Pharmacology, Vol 9 (2018)
Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search f
Externí odkaz:
https://doaj.org/article/69edc0560ee242de9d5cbae1e83447ac
Autor:
Egon L. Willighagen, John W. Mayfield, Jonathan Alvarsson, Arvid Berg, Lars Carlsson, Nina Jeliazkova, Stefan Kuhn, Tomáš Pluskal, Miquel Rojas-Chertó, Ola Spjuth, Gilleain Torrance, Chris T. Evelo, Rajarshi Guha, Christoph Steinbeck
Publikováno v:
Journal of Cheminformatics, Vol 9, Iss 1, Pp 1-1 (2017)
Externí odkaz:
https://doaj.org/article/d29f0d164e91454a8645a5397063550f
Publikováno v:
Alternatives to Laboratory Animals. 51:39-54
There is an ongoing aim to replace animal and in vitro laboratory models with in silico methods. Such replacement requires the successful validation and comparably good performance of the alternative methods. We have developed an in silico prediction
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f940a4ddc0b7e846d8641aea14793aeb
https://doi.org/10.1177/02611929221148447
https://doi.org/10.1177/02611929221148447
Publikováno v:
Journal of Pharmaceutical Sciences. 111:2614-2619
The gastrointestinal uptake of macrocyclic compounds is not fully understood. Here we applied our previously validated integrated system based on machine learning and conformal prediction to predict the passive fraction absorbed (f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::412e04abb64a96fce3e22d2e66dbd353
https://doi.org/10.1016/j.xphs.2022.05.010
https://doi.org/10.1016/j.xphs.2022.05.010
The ANDROMEDA software, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, was used to predict and characterize the human clinical pharmacokinetics of 30 selected modern small antibiotic compounds (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63cda2759bcdd75b47c8dab96742e72e
https://doi.org/10.1101/2023.03.28.534601
https://doi.org/10.1101/2023.03.28.534601
