AIMC Topic: Antineoplastic Agents
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Determination of 5-fluorouracil anticancer drug solubility in supercritical COusing semi-empirical and machine learning models.
Scientific reports
Feb 7, 2025
In order to provide the facilities to design the supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential to obtain their solubility in green solvents like pressurized CO. This important rol...
ASGCL: Adaptive Sparse Mapping-based graph contrastive learning network for cancer drug response prediction.
PLoS computational biology
Jan 30, 2025
Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to...
Machine learning and molecular subtyping reveal the impact of diverse patterns of cell death on the prognosis and treatment of hepatocellular carcinoma.
Computational biology and chemistry
Jan 27, 2025
Programmed cell death (PCD) is a significant factor in the progression of hepatocellular carcinoma (HCC) and might serve as a crucial marker for predicting HCC prognosis and therapy response. However, the classification of HCC based on diverse PCD pa...
Predicting the effectiveness of chemotherapy treatment in lung cancer utilizing artificial intelligence-supported serum N-glycome analysis.
Computers in biology and medicine
Jan 27, 2025
An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three l...
Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer.
Biochimica et biophysica acta. Molecular basis of disease
Jan 25, 2025
Drug resistance often stems from drug-tolerant persister (DTP) cells in cancer. These cells arise from various lineages and exhibit complex dynamics. However, effectively targeting DTP cells remains challenging. We used single-cell RNA sequencing (sc...
Identification of dequalinium as a potent inhibitor of human organic cation transporter 2 by machine learning based QSAR model.
Scientific reports
Jan 20, 2025
Human organic cation transporter 2 (hOCT2/SLC22A2) is a key drug transporter that facilitates the transport of endogenous and exogenous organic cations. Because hOCT2 is responsible for the development of adverse effects caused by platinum-based anti...
An in-depth review of AI-powered advancements in cancer drug discovery.
Biochimica et biophysica acta. Molecular basis of disease
Jan 19, 2025
The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep ...
Artificial Neural Network-Based Validation, DFT, Thermal and Biological Evaluation of 4-Aminoantipyrine-Derived Ru(III) Complexes.
Applied biochemistry and biotechnology
Jan 17, 2025
New methodologies have been evaluated for validating analytical characterization with artificial neural networks (ANNs). Compared to previous machine learning models, these provide more accurate and automated results with high testing accuracy. The S...
Exploring the anticancer activities of Sulfur and magnesium oxide through integration of deep learning and fuzzy rough set analyses based on the features of Vidarabine alkaloid.
Scientific reports
Jan 17, 2025
Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC 6.97 µg∕mL, 25.78 µg∕mL, and ˃ 100 µg∕mL against non-small Lung cancer (A-549), Human Melanoma (A-375), and Human epi...
MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.
Molecules (Basel, Switzerland)
Jan 12, 2025
Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs an...
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