Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Joel P. Arrais"'
Publikováno v:
Journal of Cheminformatics, Vol 16, Iss 1, Pp 1-19 (2024)
Abstract Nuclear receptors (NRs) play a crucial role as biological targets in drug discovery. However, determining which compounds can act as endocrine disruptors and modulate the function of NRs with a reduced amount of candidate drugs is a challeng
Externí odkaz:
https://doaj.org/article/c22421c89d3a4596ba148a105dceb718
Autor:
Maryam Abbasi, Beatriz P. Santos, Tiago C. Pereira, Raul Sofia, Nelson R. C. Monteiro, Carlos J. V. Simões, Rui Brito, Bernardete Ribeiro, José L. Oliveira, Joel P. Arrais
Publikováno v:
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-16 (2022)
Abstract Drug design is an important area of study for pharmaceutical businesses. However, low efficacy, off-target delivery, time consumption, and high cost are challenges and can create barriers that impact this process. Deep Learning models are em
Externí odkaz:
https://doaj.org/article/7198e0181ce94996b3f69a3a3350a466
Autor:
Nelson R. C. Monteiro, Carlos J. V. Simões, Henrique V. Ávila, Maryam Abbasi, José L. Oliveira, Joel P. Arrais
Publikováno v:
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-24 (2022)
Abstract Background Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug–target interactions and new leads. However, most of these methodologies have been overlooking the importan
Externí odkaz:
https://doaj.org/article/ce862bbbb14f4e208b0489dee1f9f22c
Publikováno v:
IEEE Access, Vol 10, Pp 78788-78817 (2022)
Many fields of study still face the challenges inherent to the analysis of complex multidimensional datasets, such as the field of computational biology, whose research of infectious diseases must contend with large protein-protein interaction networ
Externí odkaz:
https://doaj.org/article/e2130caf87ff41579ef63e1d2802e87b
Publikováno v:
Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-17 (2021)
Abstract In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose o
Externí odkaz:
https://doaj.org/article/7d1c87b5e54d42c69852b408076337ee
Autor:
Maryam Abbasi, Beatriz P. Santos, Tiago C. Pereira, Raul Sofa, Nelson R. C. Monteiro, Carlos J. V. Simões, Rui M. M. Brito, Bernardete Ribeiro, José L. Oliveira, Joel P. Arrais
Publikováno v:
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-1 (2022)
Externí odkaz:
https://doaj.org/article/ccc789eb92534ee6ab688a2fcb874b75
Publikováno v:
Biomedicines, Vol 10, Iss 2, p 315 (2022)
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative dise
Externí odkaz:
https://doaj.org/article/63dd7e4cb2264835978c72c2f4fc4655
Publikováno v:
Neural Computing and Applications. 35:13167-13185
Graph neural networks and convolutional architectures have proven to be pivotal in improving the prediction of molecular properties in drug discovery. However, this is fundamentally a low data problem that is incompatible with regular deep learning a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da0798e486838cf51b6313fe7d9a1b27
https://doi.org/10.1007/s00521-023-08403-5
https://doi.org/10.1007/s00521-023-08403-5
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a7a9517a5b148c09eee2c2d20b068c76
https://doi.org/10.1109/icassp49357.2023.10095354
https://doi.org/10.1109/icassp49357.2023.10095354
Publikováno v:
Expert Systems with Applications. 225:120005
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3b0b796ad23b096d998678867e87a8c
https://doi.org/10.1016/j.eswa.2023.120005
https://doi.org/10.1016/j.eswa.2023.120005