AIMC Topic: Antineoplastic Agents

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Exploring entropy measures with topological indices on colorectal cancer drugs using curvilinear regression analysis and machine learning approaches.

PloS one
A topological index is a numerical value derived from the structure of a molecule or graph that provides useful information about the molecule's physical, chemical, or biological properties. These indices are especially important in chemo-informatics...

Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.

Cell communication and signaling : CCS
Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC) remains a clinically challenging subtype within breast cancer classifications, marked by rapid progression and high mortality. Abnormal activation o...

Combining the NanaPPI Toolbox and AI-Driven Virtual Inhibitor Screening for the p53-MDM2 Interaction.

Analytical chemistry
High-throughput screening for inhibitors of protein-protein interactions (PPIs) provides vital information for therapeutic intervention in diseases driven by aberrant PPIs. Traditionally, the discovery of PPI inhibitors involves sequential steps: in ...

Innovations in cancer treatment: evaluating drug resistance with lab-on-a-chip technologies.

International journal of pharmaceutics
Lab-on-a-chip (LoC) technologies have emerged as transformative tools in cancer research, particularly in evaluating drug resistance, which remains a significant barrier to effective treatment. These miniaturized platforms allow for the integration o...

iACP-DPNet: a dual-pooling causal dilated convolutional network for interpretable anticancer peptide identification.

Functional & integrative genomics
Anticancer peptides (ACPs) are acknowledged for their potential in cancer therapy, attributed to their safety, low side effects, and high target specificity. However, the discovery of ACPs is slowed by the high cost and labor-intensive nature of expe...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

Scientific reports
In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements...

A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.

Nature communications
Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the limited availability of well-defined drug targets. Recent progress in generative artificial intelligen...

Predicting cisplatin response in cholangiocarcinoma patients using chromosome pattern and related gene expression.

Scientific reports
Cholangiocarcinoma (CCA) is a prevalent bile duct cancer with limited treatment options. Cisplatin-based chemotherapy is a common approach, but response rates vary. Recently, chromosome aberrations have emerged as predictors of chemotherapy response ...

Unveiling the mechanisms and promising molecular targets of curcumin in pancreatic cancer through multi-dimensional data.

Scientific reports
Pancreatic cancer (PC) is a highly aggressive and fatal malignancy, primarily affecting older males. Curcumin, a potential anti-cancer agent, has been shown to regulate key molecules in cancer progression, but its specific mechanisms in PC remain unc...

Recent advances in nanopharmaceutical strategies for cancer treatment.

Biochemical and biophysical research communications
Next-generation cancer nanomedicines are revolutionizing therapeutic precision through multifunctional, adaptive, and tumor-specific strategies. This review discusses emerging innovations in cancer nanomedicine, including stimuli-responsive nanomedic...