AIMC Topic: Antineoplastic Agents
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A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.
Bioorganic & medicinal chemistry
Nov 27, 2021
Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully trans...
Drug Discovery
Antineoplastic Agents
Cell Line, Tumor
Cell Proliferation
Machine Learning
Colorectal Neoplasms
Molecular Docking Simulation
Structure-Activity Relationship
Molecular Structure
Tumor Suppressor Protein p53
Dihydroergotoxine
Dose-Response Relationship, Drug
Cell Survival
Drug Screening Assays, Antitumor
Humans
Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning.
International journal of molecular sciences
Oct 26, 2021
The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different ...
DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.
PLoS computational biology
Oct 5, 2021
Drug treatment induces cell type specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCel...
SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.
BMC bioinformatics
Sep 10, 2021
BACKGROUND: One of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemic...
Predicting drug sensitivity of cancer cells based on DNA methylation levels.
PloS one
Sep 10, 2021
Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular featur...
Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response.
PloS one
Jul 28, 2021
Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in c...
Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction.
International journal of molecular sciences
Jul 19, 2021
Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) mo...
Recent advances in drug repurposing using machine learning.
Current opinion in chemical biology
Jul 16, 2021
Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlig...
Synthesis, docking, machine learning and antiproliferative activity of the 6-ferrocene/heterocycle-2-aminopyrimidine and 5-ferrocene-1H-Pyrazole derivatives obtained by microwave-assisted Atwal reaction as potential anticancer agents.
Bioorganic & medicinal chemistry letters
Jul 2, 2021
A simple and fast methodology under microwave irradiation for the synthesis of 2-aminopyrimidine and pyrazole derivatives using Atwal reaction is reported. After the optimization of the reaction conditions, eight 2-aminolpyrimidines containing ferroc...
Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties.
Scientific reports
Jun 30, 2021
Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity ...
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