Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship
The paper presents a comprehensive overview of the use of artificial intelligence (AI)
systems in drug design. Neural networks, which are one of the systems employed in AI, are …
systems in drug design. Neural networks, which are one of the systems employed in AI, are …
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective
Drug discovery and development is a complex and costly process. Machine learning
approaches are being investigated to help improve the effectiveness and speed of multiple …
approaches are being investigated to help improve the effectiveness and speed of multiple …
[HTML][HTML] Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches
As expenditure on drug development increases exponentially, the overall drug discovery
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …
Deep learning in drug target interaction prediction: current and future perspectives
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery.
Computational methods in DTIs prediction have gained more attention because carrying out …
Computational methods in DTIs prediction have gained more attention because carrying out …
[HTML][HTML] Drug repurposing for cancer therapy
Y Xia, M Sun, H Huang, WL Jin - Signal Transduction and Targeted …, 2024 - nature.com
Cancer, a complex and multifactorial disease, presents a significant challenge to global
health. Despite significant advances in surgical, radiotherapeutic and immunological …
health. Despite significant advances in surgical, radiotherapeutic and immunological …
[HTML][HTML] Revisiting activity of some glucocorticoids as a potential inhibitor of SARS-CoV-2 main protease: theoretical study
The global breakout of COVID-19 and raised death toll has prompted scientists to develop
novel drugs capable of inhibiting SARS-CoV-2. Conducting studies on repurposing some …
novel drugs capable of inhibiting SARS-CoV-2. Conducting studies on repurposing some …
[HTML][HTML] A multimodal deep learning-based drug repurposing approach for treatment of COVID-19
SA Hooshmand, M Zarei Ghobadi, SE Hooshmand… - Molecular …, 2021 - Springer
Recently, various computational methods have been proposed to find new therapeutic
applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach …
applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach …
A review of machine learning approaches for drug synergy prediction in cancer
A Torkamannia, Y Omidi… - Briefings in Bioinformatics, 2022 - academic.oup.com
Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment
strategy for complex diseases such as malignancies. Identifying synergistic combinations …
strategy for complex diseases such as malignancies. Identifying synergistic combinations …
MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug–target interaction prediction
R Zhang, Z Wang, X Wang, Z Meng… - Briefings in …, 2023 - academic.oup.com
Drug–target interaction (DTI) prediction can identify novel ligands for specific protein targets,
and facilitate the rapid screening of effective new drug candidates to speed up the drug …
and facilitate the rapid screening of effective new drug candidates to speed up the drug …
[HTML][HTML] A review of SARS-CoV-2 drug repurposing: Databases and machine learning models
The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2)
posed a serious worldwide threat and emphasized the urgency to find efficient solutions to …
posed a serious worldwide threat and emphasized the urgency to find efficient solutions to …