A Comprehensive Survey of Studies on Predicting Anatomical Therapeutic Chemical Classes of Drugs
P Das, DH Mazumder - ACM Computing Surveys, 2024 - dl.acm.org
Drug classification plays a crucial role in contemporary drug discovery, design, and
development. Determining the Anatomical Therapeutic Chemical (ATC) classes for new …
development. Determining the Anatomical Therapeutic Chemical (ATC) classes for new …
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 …
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 …
Overcoming catastrophic forgetting in molecular property prediction using continual learning of sequential episodes
S Ranjan, SK Singh - Expert Systems with Applications, 2025 - Elsevier
Abstract Continual Learning requires Large Language Models (LLM) to adapt to new
episodes and data over time without forgetting the knowledge acquired from previous …
episodes and data over time without forgetting the knowledge acquired from previous …
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 …
Advances in Predicting Drug Functions: A Decade-Long Survey in Drug Discovery Research
P Das, DH Mazumder - IEEE Transactions on Molecular …, 2023 - ieeexplore.ieee.org
Drug function study is vital in current drug discovery, design, and development. Determining
the drug functions of a novel drug is time-consuming, complicated, expensive, and requires …
the drug functions of a novel drug is time-consuming, complicated, expensive, and requires …
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 …
SME-MFP: A novel spatiotemporal neural network with multiangle initialization embedding toward multifunctional peptides prediction
J Xu, X Ruan, J Yang, B Hu, S Li, J Hu - Computational Biology and …, 2024 - Elsevier
As a promising alternative to conventional antibiotic drugs in the biomedical field, functional
peptide has been widely used in disease treatment owing to its low toxicity, high absorption …
peptide has been widely used in disease treatment owing to its low toxicity, high absorption …
MLCNNF: A Multi-Label Convolutional Neural Network Framework for Predicting Adverse COVID Drug Reactions From the Chemical Structure
P Das, DH Mazumder - IEEE Transactions on Computational …, 2025 - ieeexplore.ieee.org
COVID-19 is a novel disease that currently has relatively few drugs available for treatment.
Adverse COVID Drug Reactions (ACDR) are a major concern in the drug development …
Adverse COVID Drug Reactions (ACDR) are a major concern in the drug development …
K1K2NN: A novel multi-label classification approach based on neighbors for predicting COVID-19 drug side effects
P Das, DH Mazumder - Computational Biology and Chemistry, 2024 - Elsevier
Abstract COVID-19, a novel ailment, has received comparatively fewer drugs for its
treatment. Side Effects (SE) of a COVID-19 drug could cause long-term health issues …
treatment. Side Effects (SE) of a COVID-19 drug could cause long-term health issues …