AIMC Topic: Antineoplastic Agents

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

Molecular informatics Nov 28, 2019
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...
Neoplasms Peptides Molecular Docking Simulation Antineoplastic Agents Humans Neural Networks, Computer Algorithms Blood Glucose Blood Pressure
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A Bayesian machine learning approach for drug target identification using diverse data types.

Nature communications Nov 19, 2019
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...
Drug Delivery Systems Neoplasms Drug Discovery Antineoplastic Agents Humans Machine Learning Drug Repositioning Bayes Theorem
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Machine learning for target discovery in drug development.

Current opinion in chemical biology Nov 14, 2019
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...
Sesquiterpenes Molecular Targeted Therapy Transient Receptor Potential Channels Lipoxygenase Naphthoquinones Pentacyclic Triterpenes Receptors, Cannabinoid Sesquiterpenes, Guaiane Computer Simulation Antineoplastic Agents Proteomics Drug Evaluation, Preclinical Humans Machine Learning
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System-level responses to cisplatin in pro-apoptotic stages of breast cancer MCF-7 cell line.

Computational biology and chemistry Nov 2, 2019
Cisplatin ceases cell division and induces apoptosis in cancer cell lines. It is well established that cisplatin alters the expression of many genes involved in several cellular processes and pathways including transcription, p53 signaling pathway, a...
Drug Discovery Antineoplastic Agents Protein Interaction Maps Gene Regulatory Networks Humans Software Female Breast Neoplasms Drug Screening Assays, Antitumor MCF-7 Cells Cisplatin Gene Ontology Gene Expression Regulation, Neoplastic Cell Proliferation Apoptosis Neoplasm Proteins
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Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning.

Analytica chimica acta Sep 25, 2019
Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absen...
Irinotecan HCT116 Cells Proof of Concept Study Drug Resistance Neural Networks, Computer Machine Learning Biomarkers Antineoplastic Agents ROC Curve Single-Cell Analysis Mass Spectrometry Metabolomics Humans
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Designing combination therapies with modeling chaperoned machine learning.

PLoS computational biology Sep 9, 2019
Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational d...
Drug Therapy, Computer-Assisted Neoplasms Antineoplastic Agents Computational Biology Signal Transduction Models, Biological Cisplatin Drug Therapy, Combination TNF-Related Apoptosis-Inducing Ligand Humans Machine Learning Algorithms
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Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BMC bioinformatics Jul 29, 2019
BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic...
Deep Learning Cell Line, Tumor Genomics Inhibitory Concentration 50 Humans Phenotype Regression Analysis Organ Specificity Neural Networks, Computer Databases, Factual Neoplasms Antineoplastic Agents
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Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

The journal of physical chemistry letters Jul 22, 2019
It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, ...
Binding Sites Proto-Oncogene Proteins c-mdm2 Cyclin-Dependent Kinase 6 Databases, Pharmaceutical Matrix Metalloproteinase 13 Mutation Peptides Ligands Drug Design Proto-Oncogene Proteins c-bcl-2 Antineoplastic Agents Tumor Suppressor Protein p53 Deep Learning Cyclin-Dependent Kinase Inhibitor p16 Machine Learning Proteins Algorithms Databases, Protein Models, Molecular
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HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.

Scientific reports Jul 3, 2019
Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have bee...
Humans Artificial Intelligence Databases, Factual Drug Repositioning Diet Metabolic Networks and Pathways Food Neoplasms Antineoplastic Agents Food Analysis
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Error Tolerance of Machine Learning Algorithms across Contemporary Biological Targets.

Molecules (Basel, Switzerland) Jun 4, 2019
Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two l...
Antineoplastic Agents Algorithms Models, Biological ROC Curve Humans Molecular Targeted Therapy Reproducibility of Results Bayes Theorem Neural Networks, Computer Biomarkers, Tumor Workflow Machine Learning Drug Discovery
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