AIMC Topic: Antineoplastic Agents
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Artificial Intelligence and Cancer Clinical Research: III Risk Prediction Models for Febrile Neutropenia in Patients Receiving Cancer Chemotherapy.
Cancer investigation
Jul 4, 2024
Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.
Journal of chemical information and modeling
Jul 3, 2024
In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuraci...
Application of Photoactive Compounds in Cancer Theranostics: Review on Recent Trends from Photoactive Chemistry to Artificial Intelligence.
Molecules (Basel, Switzerland)
Jul 3, 2024
According to the World Health Organization (WHO) and the International Agency for Research on Cancer (IARC), the number of cancer cases and deaths worldwide is predicted to nearly double by 2030, reaching 21.7 million cases and 13 million fatalities....
Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides-Triazole Hybrids with Anticancer Activity.
Molecules (Basel, Switzerland)
Jul 2, 2024
In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed and synthesised. The title compounds were designed using molecular modelling techniques. For this pur...
A high hydrophobic moment arginine-rich peptide screened by a machine learning algorithm enhanced ADC antitumor activity.
Journal of peptide science : an official publication of the European Peptide Society
Jul 1, 2024
Cell-penetrating peptides (CPPs) with better biomolecule delivery properties will expand their clinical applications. Using the MLCPP2.0 machine algorithm, we screened multiple candidate sequences with potential cellular uptake ability from the nucle...
mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations.
Journal of molecular biology
Jun 25, 2024
Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the pa...
Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.
Nature communications
Jun 25, 2024
Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles ...
From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.
ChemMedChem
Jun 20, 2024
Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effe...
Protein Kinase Inhibitors
Structure-Activity Relationship
Molecular Structure
Vascular Endothelial Growth Factor Receptor-2
Drug Discovery
Humans
Dose-Response Relationship, Drug
Antineoplastic Agents
Cell Line, Tumor
Molecular Docking Simulation
Drug Screening Assays, Antitumor
Deep Learning
Cell Proliferation
Artificial intelligence for small molecule anticancer drug discovery.
Expert opinion on drug discovery
Jun 18, 2024
INTRODUCTION: The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite th...
Harnessing machine learning potential for personalised drug design and overcoming drug resistance.
Journal of drug targeting
Jun 13, 2024
Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing comple...
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