Results 21 to 30 of about 2,729,950 (336)

Cytokine–receptor interactions as drug targets [PDF]

Current Opinion in Chemical Biology, 2010
Cytokines are essential proteins that exert potent control over entire cell populations to fight infections and other pathologies, but can by themselves cause disease. Therefore, cytokine-related drugs act either by stimulating or blocking their activities.
Schreiber, Gideon, Walter, Mark R
openaire   +2 more sources

Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

Big Data and Cognitive Computing, 2021
Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an ...
Aulia Fadli, Wisnu Ananta Kusuma, Annisa, Irmanida Batubara, Rudi Heryanto   +4 more
doaj   +1 more source

Toward more realistic drug-target interaction predictions [PDF]

Briefings in Bioinformatics, 2014
A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction ...
Tapio Pahikkala, Antti Airola, Sami Pietilä, Sushil Shakyawar, Agnieszka Szwajda, Jing Tang, Tero Aittokallio   +6 more
openaire   +2 more sources

UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning

Molecules, 2022
Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency.
Aida Tayebi, Niloofar Yousefi, Mehdi Yazdani-Jahromi, Elayaraja Kolanthai, Craig J. Neal, Sudipta Seal, Ozlem Ozmen Garibay   +6 more
doaj   +1 more source

DeepDrug: A general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction

Quantitative Biology, 2023
Computational methods for DDIs and DTIs prediction are essential for accelerating the drug discovery process. We proposed a novel deep learning method DeepDrug, to tackle these two problems within a unified framework.
Qijin Yin, Rui Fan, Xusheng Cao, Qiao Liu, Rui Jiang, Wanwen Zeng   +5 more
doaj   +1 more source

Gaussian interaction profile kernels for predicting drug–target interaction [PDF]

Bioinformatics, 2011
Abstract Motivation: The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug–target pairs in current datasets are experimentally validated interactions.
Laarhoven, T.M. van, Nabuurs, S.B., Marchiori, E.   +2 more
openaire   +5 more sources

AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification

Briefings Bioinform., 2022
In this study, we introduce an interpretable graph-based deep learning prediction model, AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism to address the problem of drug–target interaction prediction.
Mehdi Yazdani-Jahromi, Niloofar Yousefi, Aida Tayebi, Elayaraja Kolanthai, Craig J. Neal, Sudipta Seal, O. Garibay   +6 more
semanticscholar   +1 more source

A network inference method for large-scale unsupervised identification of novel drug-drug interactions [PDF]

, 2013
Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others.
Guimera, Roger, Sales-Pardo, Marta
core   +4 more sources

A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

Nature Communications, 2017
The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a ...
Yunan Luo, Xinbin Zhao, Jingtian Zhou, Jinglin Yang, Yanqing Zhang, Wenhua Kuang, Jian Peng, Ligong Chen, Jianyang Zeng   +8 more
semanticscholar   +1 more source

Identification of drug-target interaction by a random walk with restart method on an interactome network

BMC Bioinformatics, 2018
Background Identification of drug-target interactions acts as a key role in drug discovery. However, identifying drug-target interactions via in-vitro, in-vivo experiments are very laborious, time-consuming.
Ingoo Lee, Hojung Nam
doaj   +1 more source