AIMC Topic: Databases, Pharmaceutical
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An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).
Scientific reports
Apr 16, 2025
The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of el...
Humans
Reproducibility of Results
Deep Learning
Big Data
Diarrhea
ROC Curve
Gastrointestinal Tract
Electrodiagnosis
Constipation
Digestive System Diseases
Nausea
Predictive Learning Models
Area Under Curve
Feasibility Studies
Drug-Related Side Effects and Adverse Reactions
Vomiting
Databases, Pharmaceutical
Biological Clocks
RECOMED: A comprehensive pharmaceutical recommendation system.
Artificial intelligence in medicine
Sep 12, 2024
OBJECTIVES: To build datasets containing useful information from drug databases and recommend a list of drugs to physicians and patients with high accuracy by considering a wide range of features of people, diseases, and chemicals.
Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.
Methods (San Diego, Calif.)
Mar 20, 2024
The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper uti...
Predicting lifespan-extending chemical compounds for with machine learning and biologically interpretable features.
Aging
Jul 13, 2023
Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse da...
Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD).
Scientific reports
Apr 28, 2023
Electrical data could be a new source of big-data for training artificial intelligence (AI) for drug discovery. A Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) was built using a standardized methodology to test drug effects on electrica...
The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.
GigaScience
Mar 9, 2023
BACKGROUND: Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research...
An annotated corpus from biomedical articles to construct a drug-food interaction database.
Journal of biomedical informatics
Jan 7, 2022
MOTIVATION: While drug-food interaction (DFI) may undermine the efficacy and safety of drugs, DFI detection has been difficult because a well-organized database for DFI did not exist. To construct a DFI database and build a natural language processin...
IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.
International journal of molecular sciences
Dec 2, 2021
The parasite species of genus causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds...
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 ...
Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods.
Chemical biology & drug design
Oct 11, 2021
The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the β-site. The sequential proteolytic cleavage of APP, first by β-secretase and then by γ-secreta...
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