Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Arash Keshavarzi Arshadi"'
Publikováno v:
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-17 (2022)
Abstract Background Deep learning’s automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data to train, which in the case of
Externí odkaz:
https://doaj.org/article/604701393cb2494aa8c8d2de130071e3
Publikováno v:
Journal of Cheminformatics, Vol 14, Iss 1, Pp 1-18 (2022)
Abstract Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molec
Externí odkaz:
https://doaj.org/article/f9d3795795bd4b319b61df74463f928f
Autor:
Arash Keshavarzi Arshadi, Julia Webb, Milad Salem, Emmanuel Cruz, Stacie Calad-Thomson, Niloofar Ghadirian, Jennifer Collins, Elena Diez-Cecilia, Brendan Kelly, Hani Goodarzi, Jiann Shiun Yuan
Publikováno v:
Frontiers in Artificial Intelligence, Vol 3 (2020)
SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Underst
Externí odkaz:
https://doaj.org/article/dc313295629747798bfad07f2859c280
Autor:
Arash Keshavarzi Arshadi, Milad Salem, Jennifer Collins, Jiann Shiun Yuan, Debopam Chakrabarti
Publikováno v:
Frontiers in Pharmacology, Vol 10 (2020)
Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The tradition
Externí odkaz:
https://doaj.org/article/178e3e8e90164e7ba5a9322b5c16c916
Publikováno v:
Big Data and Cognitive Computing, Vol 4, Iss 3, p 16 (2020)
Deep learning’s automatic feature extraction has proven its superior performance over traditional fingerprint-based features in the implementation of virtual screening models. However, these models face multiple challenges in the field of early dru
Externí odkaz:
https://doaj.org/article/323726d38eec472eb070eb52c332a732
Autor:
Arash Keshavarzi Arshadi, Milad Salem, Heather Karner, Kristle Garcia, Abolfazl Arab, Jiann Shiun Yuan, Hani Goodarzi
Publikováno v:
bioRxiv
MicroRNAs are recognized as key drivers in many cancers, but targeting them with small molecules remains a challenge. We present RiboStrike, a deep learning framework that identifies small molecules against specific microRNAs. To demonstrate its capa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9655835cdf32e9c32a82ce1404bf1c5
https://europepmc.org/articles/PMC9882104/
https://europepmc.org/articles/PMC9882104/
Autor:
Jin Woo Lee, Robert H. Cichewicz, Jennifer Collins, Lin Du, Monica J Bohmer, Joshua D Welden, Debopam Chakrabarti, Arash Keshavarzi Arshadi
Publikováno v:
ACS Infectious Diseases. 7:2889-2903
Cyclic tetrapeptide histone deacetylase inhibitors represent a promising class of antiplasmodial agents that epigenetically disrupt a wide range of cellular processes in Plasmodium falciparum. Unfortunately, certain limitations, including reversible
Background Deep learning’s automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data to train, which in the case of Hemolytic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ddb14ec9ea16a912d20814eaf2b46a29
https://doi.org/10.21203/rs.3.rs-1615895/v1
https://doi.org/10.21203/rs.3.rs-1615895/v1
Autor:
Guang Huang, Jennifer Collins, Yu Yuan, Justin Shaw, Joel Melendez, Rachasak Boonhok, Hui Min, Claribel Murillo Solano, Jun Miao, Debopam Chakrabarti, Robert K. Banks, Arash Keshavarzi Arshadi
Publikováno v:
J Med Chem
There is an urgent need to develop new efficacious antimalarials to address the emerging drug-resistant clinical cases. Our previous phenotypic screening identified styrylquinoline UCF501 as a promising antimalarial compound. To optimize UCF501, we h