Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions
Neurological disorders significantly outnumber diseases in other therapeutic areas.
However, developing drugs for central nervous system (CNS) disorders remains the most …
However, developing drugs for central nervous system (CNS) disorders remains the most …
[HTML][HTML] On the road to explainable AI in drug-drug interactions prediction: A systematic review
Over the past decade, polypharmacy instances have been common in multi-diseases
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected …
An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects
P Das, DH Mazumder - Artificial Intelligence Review, 2023 - Springer
Approved drugs for sale must be effective and safe, implying that the drug's advantages
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …
DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions
Adverse drug-drug interaction (DDI) is a major concern to polypharmacy due to its
unexpected adverse side effects and must be identified at an early stage of drug discovery …
unexpected adverse side effects and must be identified at an early stage of drug discovery …
MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional …
P Das, DH Mazumder - ETRI Journal, 2024 - Wiley Online Library
To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been
approved. Due to the global pandemic status of COVID, several medicines are being …
approved. Due to the global pandemic status of COVID, several medicines are being …
DIGEP‐Pred 2.0: A web application for predicting drug‐induced cell signaling and gene expression changes
SM Ivanov, AV Rudik, AA Lagunin… - Molecular …, 2024 - Wiley Online Library
The analysis of drug‐induced gene expression profiles (DIGEP) is widely used to estimate
the potential therapeutic and adverse drug effects as well as the molecular mechanisms of …
the potential therapeutic and adverse drug effects as well as the molecular mechanisms of …
ADENet: a novel network-based inference method for prediction of drug adverse events
Identification of adverse drug events (ADEs) is crucial to reduce human health risks and
improve drug safety assessment. With an increasing number of biological and medical data …
improve drug safety assessment. With an increasing number of biological and medical data …
Identify unfavorable covid medicine reactions from the three-dimensional structure by employing convolutional neural network
P Das, DH Mazumder - Mathematical Modeling and Intelligent Control for …, 2023 - Springer
The medicine development process is expensive, challenging, and time needed.
Computational model-based classifiers have been employed to overcome these problems …
Computational model-based classifiers have been employed to overcome these problems …
Inceptionv3‐LSTM‐COV: A multi‐label framework for identifying adverse reactions to COVID medicine from chemical conformers based on Inceptionv3 and long short …
P Das, DH Mazumder - ETRI Journal, 2024 - Wiley Online Library
Due to the global COVID‐19 pandemic, distinct medicines have been developed for treating
the coronavirus disease (COVID). However, predicting and identifying potential adverse …
the coronavirus disease (COVID). However, predicting and identifying potential adverse …
Bert-d2: Drug-drug interaction extraction using bert
When one drug interacts with another, it is known as a drug-drug interaction. This could
change how one or both drugs work in the body, or induce unforeseen adverse effects. The …
change how one or both drugs work in the body, or induce unforeseen adverse effects. The …