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 …
Artificial intelligence assists precision medicine in cancer treatment
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the
same drugs or surgical methods in patients with the same tumor may have different curative …
same drugs or surgical methods in patients with the same tumor may have different curative …
Gene expression based inference of cancer drug sensitivity
S Chawla, A Rockstroh, M Lehman, E Ratther… - Nature …, 2022 - nature.com
Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer
and are responsible for imparting differential drug responses in cancer patients. Recently …
and are responsible for imparting differential drug responses in cancer patients. Recently …
Advances of artificial intelligence in anti-cancer drug design: a review of the past decade
L Wang, Y Song, H Wang, X Zhang, M Wang, J He… - Pharmaceuticals, 2023 - mdpi.com
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-
consuming, and challenging task. How to reduce the research costs and speed up the …
consuming, and challenging task. How to reduce the research costs and speed up the …
Forecasting in financial accounting with artificial intelligence–A systematic literature review and future research agenda
M Kureljusic, E Karger - Journal of Applied Accounting Research, 2023 - emerald.com
Purpose Accounting information systems are mainly rule-based, and data are usually
available and well-structured. However, many accounting systems are yet to catch up with …
available and well-structured. However, many accounting systems are yet to catch up with …
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction
Predicting the response of a cancer cell line to a therapeutic drug is an important topic in
modern oncology that can help personalized treatment for cancers. Although numerous …
modern oncology that can help personalized treatment for cancers. Although numerous …
A review on recent trends in the phosphoproteomics workflow. From sample preparation to data analysis
J Urban - Analytica Chimica Acta, 2022 - Elsevier
Phosphorylation is one of the quickest post-translational modifications that controls
downstream signaling pathways regulating processes like cell proliferation, survival, and …
downstream signaling pathways regulating processes like cell proliferation, survival, and …
Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism
Identifying synergistic drug combinations (SDCs) is a great challenge due to the
combinatorial complexity and the fact that SDC is cell line specific. The existing …
combinatorial complexity and the fact that SDC is cell line specific. The existing …
A cross-study analysis of drug response prediction in cancer cell lines
To enable personalized cancer treatment, machine learning models have been developed
to predict drug response as a function of tumor and drug features. However, most algorithm …
to predict drug response as a function of tumor and drug features. However, most algorithm …
The future of precision oncology
Our understanding of the molecular mechanisms underlying cancer development and
evolution have evolved rapidly over recent years, and the variation from one patient to …
evolution have evolved rapidly over recent years, and the variation from one patient to …