AIMC Topic: Antineoplastic Agents

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Machine and deep learning approaches for cancer drug repurposing.

Seminars in cancer biology
Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targ...

Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma.

Nature reviews. Gastroenterology & hepatology
Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agentsĀ have demonstrated efficacy in clinical trials, including the targeted thera...

Propofol affects the growth and metastasis of pancreatic cancer via ADAM8.

Pharmacological reports : PR
BACKGROUND: Anesthesia is a major component of surgery and recently considered an important regulator of cell phenotypes. Here we aimed to investigate propofol, an anesthesia drug, in suppressing pancreatic cancer (PDAC), focusing on A disintegrin an...

Efficient identification of novel anti-glioma lead compounds by machine learning models.

European journal of medicinal chemistry
Glioblastoma multiforme (GBM) is the most devastating and widespread primary central nervous system tumor. Pharmacological treatment of this malignance is limited by the selective permeability of the blood-brain barrier (BBB) and relies on a single d...

Latest trends in structure based drug design with protein targets.

Advances in protein chemistry and structural biology
Structure based drug designing is an important endeavor in the field of structural bioinformatics. Previously the entire process was dependent on the wet-lab experiments to build libraries of ligand molecules. And the molecules used to be tested to d...

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients.

International journal of molecular sciences
Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecu...

Synthesis and biological characterization of MnZnEuDyFeO nanoparticles by sonochemical approach.

Materials science & engineering. C, Materials for biological applications
Metallic nanoparticles (NPs) possess unique properties which makes them attractive candidates for various applications especially in field of experimental medicine and drug delivery. Many approaches were developed to synthesize divers and customized ...

Prediction of the Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph Convolutional Neural Network.

Molecular informatics
Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In t...

A Bayesian machine learning approach for drug target identification using diverse data types.

Nature communications
Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating...

Machine learning for target discovery in drug development.

Current opinion in chemical biology
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursu...