Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Joris Cadow"'
Autor:
Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-6 (2023)
Abstract With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of
Externí odkaz:
https://doaj.org/article/d563c21247234f54b23d37c88d05e3d1
Autor:
Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, Greta Markert, María Rodríguez Martínez
Publikováno v:
iScience, Vol 24, Iss 4, Pp 102269- (2021)
Summary: With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical p
Externí odkaz:
https://doaj.org/article/2878649a15784acc80e2b1f5cc274279
Autor:
Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of novel hy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::145b068669f6e0ef69f5e03844536585
Autor:
Matteo Manica, Roland Mathis, Charlotte Bunne, María Rodríguez Martínez, Joris Cadow, Mehmet Eren Ahsen, Gustavo Stolovitzky
Publikováno v:
Bioinformatics, 37 (14)
Bioinformatics
Bioinformatics
The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput da
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3554baa0a8ef1f3580192f558f3e1cee
https://hdl.handle.net/20.500.11850/503348
https://hdl.handle.net/20.500.11850/503348
Publikováno v:
Nature Machine Intelligence, 1 (4)
The number of biomedical publications has grown steadily in recent years. However, most biomedical facts are not readily available, but buried in the form of unstructured text. Here we present INtERAcT, an unsupervised method to extract interactions
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd8e9aee83e6d5f567990d24504a6678
https://doi.org/10.1038/s42256-019-0036-1
https://doi.org/10.1038/s42256-019-0036-1
Autor:
Ali Oskooei, Joris Cadow, Matteo Manica, Greta Markert, Jannis Born, María Rodríguez Martínez
Publikováno v:
iScience, Vol 24, Iss 4, Pp 102269-(2021)
iScience, 24 (4)
iScience, 24 (4)
With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical properties
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29b5bdda86e945d29a406fbc7d4e45d0
http://www.sciencedirect.com/science/article/pii/S2589004221002376
http://www.sciencedirect.com/science/article/pii/S2589004221002376
Autor:
Teodoro Laino, Antonio Cardinale, Nil Adell Mill, María Rodríguez Martínez, Nikita Janakarajan, Modestas Filipavicius, Jannis Born, Joris Cadow, Greta Markert, Matteo Manica
Publikováno v:
Machine Learning: Science and Technology
Machine Learning: Science and Technology, 2 (2)
Machine Learning: Science and Technology, 2 (2)
Bridging systems biology and drug design, we propose a deep learning framework for de novo discovery of molecules tailored to bind with given protein targets. Our methodology is exemplified by the task of designing antiviral candidates to target SARS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ceef35fcd5b24d9daa93247961865373
Autor:
Jannis, Born, Matteo, Manica, Ali, Oskooei, Joris, Cadow, Greta, Markert, María, Rodríguez Martínez
Publikováno v:
iScience
Summary With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::bbca34b85a54294fff12f34b3ea79969
https://pubmed.ncbi.nlm.nih.gov/33851095
https://pubmed.ncbi.nlm.nih.gov/33851095
Publikováno v:
Nucleic Acids Research
Nucleic Acids Research, 48 (W1)
Nucleic Acids Research, 48 (W1)
The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81b6bfcdb935bed13a6675c5c639ecdf
https://zenodo.org/record/3935564
https://zenodo.org/record/3935564
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030452568
RECOMB
RECOMB
The pharmaceutical industry has experienced a significant productivity decline: Less than 0.01% of drug candidates obtain market approval, with an estimated 10–15 years until market release and costs that range between one [2] to three billion doll
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::01b0e3e95d74a61eb522ed9ca20c8498
https://doi.org/10.1007/978-3-030-45257-5_18
https://doi.org/10.1007/978-3-030-45257-5_18