Challenges and opportunities in cancer drug resistance
RA Ward, S Fawell, N Floc'h, V Flemington… - Chemical …, 2020 - ACS Publications
There has been huge progress in the discovery of targeted cancer therapies in recent years.
However, even for the most successful and impactful cancer drugs which have been …
However, even for the most successful and impactful cancer drugs which have been …
Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …
However, developing drugs for central nervous system (CNS) disorders remains the most …
CancerGPT for few shot drug pair synergy prediction using large pretrained language models
Large language models (LLMs) have been shown to have significant potential in few-shot
learning across various fields, even with minimal training data. However, their ability to …
learning across various fields, even with minimal training data. However, their ability to …
SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction
Synergistic drug combinations can improve the therapeutic effect and reduce the drug
dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen …
dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen …
Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction
Motivation Drug combinations have exhibited promise in treating cancers with less toxicity
and fewer adverse reactions. However, in vitro screening of synergistic drug combinations is …
and fewer adverse reactions. However, in vitro screening of synergistic drug combinations is …
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 …
SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
Background In cancer research, high-throughput screening technologies produce large
amounts of multiomics data from different populations and cell types. However, analysis of …
amounts of multiomics data from different populations and cell types. However, analysis of …
Machine learning approaches for drug combination therapies
B Güvenç Paltun, S Kaski… - Briefings in …, 2021 - academic.oup.com
Drug combination therapy is a promising strategy to treat complex diseases such as cancer
and infectious diseases. However, current knowledge of drug combination therapies …
and infectious diseases. However, current knowledge of drug combination therapies …
[HTML][HTML] Computer-aided drug repurposing for cancer therapy: approaches and opportunities to challenge anticancer targets
Despite huge efforts made in academic and pharmaceutical worldwide research, current
anticancer therapies achieve effective treatment in a limited number of neoplasia cases only …
anticancer therapies achieve effective treatment in a limited number of neoplasia cases only …
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy
SR Hosseini, X Zhou - Briefings in bioinformatics, 2023 - academic.oup.com
Combination therapy is a promising strategy for confronting the complexity of cancer.
However, experimental exploration of the vast space of potential drug combinations is costly …
However, experimental exploration of the vast space of potential drug combinations is costly …