AIMC Topic: Antineoplastic Agents

Clear Filters Showing 451 to 460 of 592 articles

Machine Learning Model for Predicting Sertraline-like Activities and Its Impact on Cancer Chemosensitization.

ACS chemical neuroscience
Selective serotonin reuptake inhibitors (SSRIs) like sertraline are crucial in treating depression and anxiety disorders, and studies indicate their potential as chemosensitizers in cancer therapy. This research develops a machine-learning predictive...

Machine learning driven prediction of drug efficacy in lung cancer: based on protein biomarkers and clinical features.

Life sciences
Currently, chemotherapy drugs are the first-line treatment for lung cancer patients, and evaluating their efficacy is of utmost significance. However, assessing the clinical efficacy of chemotherapy drugs remains a challenging task. In recent years, ...

Natural Language Processing-Based Approach to Detect Common Adverse Events of Anticancer Agents from Unstructured Clinical Notes: A Time-to-Event Analysis.

Studies in health technology and informatics
This study assessed the effectiveness of natural language processing (NLP) in detecting adverse events (AEs) from anticancer agents by analyzing data from over 39,000 cancer patients. A specialized machine learning model identified known AEs from ant...

The Construction of a New Prognostic Model of Breast Cancer and the Exploration of Drug Sensitivity Based on Machine Learning for Glycosylation-Related Genes.

Clinical breast cancer
AIMS: Breast cancer has become the number 1 killer threatening women's health. In recent years, glycosylation modification has played an increasingly important role in tumor progression. The aim of this study was to explore the key genes that may be ...

In silico design strategies for tubulin inhibitors for the development of anticancer therapies.

Expert opinion on drug discovery
INTRODUCTION: Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, ne...

pACPs-DNN: Predicting anticancer peptides using novel peptide transformation into evolutionary and structure matrix-based images with self-attention deep learning model.

Computational biology and chemistry
Globally, cancer remains a major health challenge due to its high mortality rates. Traditional experimental approaches and therapies are resource-intensive and often cause significant side effects. Anticancer peptides (ACPs) have emerged as alternati...

The Icarian flight of antibody-drug conjugates: target selection amidst complexity and tackling adverse impacts.

Protein & cell
Antibody-drug conjugates (ADCs) represent a promising class of targeted cancer therapeutics that combine the specificity of monoclonal antibodies with the potency of cytotoxic payloads. Despite their therapeutic potential, the use of ADCs faces signi...

Computer-aided drug discovery of a dual-target inhibitor for ovarian cancer: therapeutic intervention targeting CDK1/TTK signaling pathway and structural insights in the NCI-60.

Computers in biology and medicine
Ovarian cancer remains the third most prevalent and deadliest gynecologic malignancy worldwide, with most patients eventually developing resistance to platinum-based chemotherapy. This highlights a critical unmet need for innovative multitargeted the...

Extracellular vesicles as nature's nano carriers in cancer therapy: Insights toward preclinical studies and clinical applications.

Pharmacological research
Extracellular vesicles (EVs), which are secreted by various cell types, hold significant potential for cancer therapy. However, there are several challenges and difficulties that limit their application in clinical settings. This review, which integr...