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Deep learning in drug discovery: an integrative review and future challenges
Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of develo** new drugs. Deep learning (DL) …
since it significantly shortens the time and cost of develo** new drugs. Deep learning (DL) …
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
Independent drug action in combination therapy: implications for precision oncology
Combination therapies are superior to monotherapy for many cancers. This advantage was
historically ascribed to the ability of combinations to address tumor heterogeneity, but …
historically ascribed to the ability of combinations to address tumor heterogeneity, but …
Artificial intelligence and machine learning based intervention in medical infrastructure: a review and future trends
People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning
(ML) are under increased pressure to develop algorithms faster than ever. The possibility of …
(ML) are under increased pressure to develop algorithms faster than ever. The possibility of …
Harnessing machine learning to find synergistic combinations for FDA-approved cancer drugs
Combination therapy is a fundamental strategy in cancer chemotherapy. It involves
administering two or more anti-cancer agents to increase efficacy and overcome multidrug …
administering two or more anti-cancer agents to increase efficacy and overcome multidrug …
DeepTraSynergy: drug combinations using multimodal deep learning with transformers
Motivation Screening bioactive compounds in cancer cell lines receive more attention.
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
Machine learning methods, databases and tools for drug combination prediction
L Wu, Y Wen, D Leng, Q Zhang, C Dai… - Briefings in …, 2022 - academic.oup.com
Combination therapy has shown an obvious efficacy on complex diseases and can greatly
reduce the development of drug resistance. However, even with high-throughput screens …
reduce the development of drug resistance. However, even with high-throughput screens …
DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal
Combinatorial therapies that target multiple pathways have shown great promises for
treating complex diseases. DrugComb (https://drugcomb. org/) is a web-based portal for the …
treating complex diseases. DrugComb (https://drugcomb. org/) is a web-based portal for the …
[HTML][HTML] Machine learning in the prediction of cancer therapy
Resistance to therapy remains a major cause of cancer treatment failures, resulting in many
cancer-related deaths. Resistance can occur at any time during the treatment, even at the …
cancer-related deaths. Resistance can occur at any time during the treatment, even at the …
Research on disease prediction based on improved DeepFM and IoMT
Z Yu, SU Amin, M Alhussein, Z Lv - IEEE Access, 2021 - ieeexplore.ieee.org
In recent years, with the increase of computer computing power, Deep Learning has begun
to be favored. Its learning of non-linear feature combinations has played a role that …
to be favored. Its learning of non-linear feature combinations has played a role that …