AIMC Topic: Antineoplastic Agents
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The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects.
Pharmacological research
Aug 30, 2024
Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug ...
Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds.
Journal of chemical information and modeling
Aug 28, 2024
Recently, various modern experimental screening pipelines and assays have been developed to find promising anticancer drug candidates. However, it is time-consuming and almost infeasible to screen an immense number of compounds for anticancer activit...
Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery.
International journal of biological macromolecules
Aug 27, 2024
This study aims to develop sorafenib-loaded self-assembled nanoparticles (SFB-SANPs) using the combined approach of artificial neural network and design of experiments (ANN-DoE) and to compare it with other machine learning (ML) models. The central c...
Deep reinforcement learning control of combined chemotherapy and anti-angiogenic drug delivery for cancerous tumor treatment.
Computers in biology and medicine
Aug 23, 2024
By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on nor...
MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.
Biomolecules
Aug 22, 2024
Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships bet...
Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.
Scientific reports
Aug 21, 2024
The druggable proteome refers to proteins that can bind to small molecules with appropriate chemical affinity, inducing a favorable clinical response. Predicting druggable proteins through screening and in silico modeling is imperative for drug desig...
Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review.
Clinical pharmacokinetics
Aug 17, 2024
INTRODUCTION: In the last decade, various Machine Learning techniques have been proposed aiming to individualise the dose of anticancer drugs mostly based on a presumed drug effect or measured effect biomarkers. The aim of this scoping review was to ...
Chemical analogue based drug design for cancer treatment targeting PI3K: integrating machine learning and molecular modeling.
Molecular diversity
Aug 17, 2024
Cancer is a generic term for a group of disorders defined by uncontrolled cell growth and the potential to invade or spread to other parts of the body. Gene and epigenetic alterations disrupt normal cellular control, leading to abnormal cell prolifer...
Ligands
Protein Kinase Inhibitors
Phosphoinositide-3 Kinase Inhibitors
Models, Molecular
Phosphatidylinositol 3-Kinases
Molecular Dynamics Simulation
Class Ib Phosphatidylinositol 3-Kinase
Machine Learning
Neoplasms
Molecular Docking Simulation
Drug Design
Antineoplastic Agents
Protein Binding
Humans
Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.
Journal of controlled release : official journal of the Controlled Release Society
Aug 16, 2024
Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tum...
Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas.
Journal of translational medicine
Aug 16, 2024
BACKGROUND: Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, ...
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