Results 31 to 40 of about 2,729,950 (336)

Drug-target interaction prediction using semi-bipartite graph model and deep learning

BMC Bioinformatics, 2020
Background Identifying drug-target interaction is a key element in drug discovery. In silico prediction of drug-target interaction can speed up the process of identifying unknown interactions between drugs and target proteins.
Hafez Eslami Manoochehri, Mehrdad Nourani   +1 more
doaj   +1 more source

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Computational and Structural Biotechnology Journal, 2020
Highlights • The MT-DTI deep learning model was used to identify potent drugs for SARS-CoV-2.• Atazanavir, remdesivir, and Kaletra were predicted to inhibit SARS-CoV-2.• Rapamycin and tiotropium bromide may also be effective for SARS-CoV-2.
B. Beck, Bonggun Shin, Yoonjung Choi, Sungsoo Park, Keunsoo Kang   +4 more
semanticscholar   +1 more source

Computational Drug Target Screening through Protein Interaction Profiles [PDF]

Scientific Reports, 2016
AbstractThe development of computational methods to discover novel drug-target interactions on a large scale is of great interest. We propose a new method for virtual screening based on protein interaction profile similarity to discover new targets for molecules, including existing drugs.
Vilar Varela, Santiago, Quezada González, Elías Neftalí, Uriarte Villares, Eugenio, Costanzi, Stefano, Borges, Fernanda, Viña Castelao, María Dolores, Hripcsak, George   +6 more
openaire   +3 more sources

Benchmarking network propagation methods for disease gene identification [PDF]

, 2019
In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information.
Barrett, Steven J., Dessailly, Benoit H., Gutteridge, Alex, Perera Lluna, Alexandre, Picart Armada, Sergio, Willé, David R.   +5 more
core   +2 more sources

Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile. [PDF]

PLoS ONE, 2013
In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction.
Twan van Laarhoven, Elena Marchiori
doaj   +1 more source

Systematic Exploration of Synergistic Drug Pairs [PDF]

, 2013
Drug synergy allows a therapeutic effect to be achieved with lower doses of component drugs. Drug synergy can result when drugs target the products of genes that act in parallel pathways (‘specific synergy’).
Andrews, Brenda J, Baryshnikova, Anastasia, Boone, Charles, Chua, Hon Nian, Cokol, Murat, Costanzo, Michael, Giaever, Guri, Mutlu, Beste, Myers, Chad L, Nergiz, Mehmet E, Nislow, Corey, Roth, Frederick Phillip, Suzuki, Yo, Tasan, Murat, Weinstein, Zohar B   +14 more
core   +1 more source

Drug Target Commons 2.0: a community platform for systematic analysis of drug–target interaction profiles [PDF]

Database, 2018
Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an ...
Tanoli, Ziaur Rehman, Alam, Zaid, Vähä-Koskela, Markus, Ravikumar, Balaguru, Malyutina, Alina, Jaiswal, Alok, Tang, Jing, Wennerberg, Krister, Aittokallio, Tero   +8 more
openaire   +6 more sources

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction

BMC Bioinformatics, 2023
Background Prediction of drug–target interaction (DTI) is an essential step for drug discovery and drug reposition. Traditional methods are mostly time-consuming and labor-intensive, and deep learning-based methods address these limitations and are ...
Ying Qian, Xinyi Li, Jian Wu, Q. Zhang
semanticscholar   +1 more source

Predicting Drug-Target Interactions Using Drug-Drug Interactions

PLoS ONE, 2013
Computational methods for predicting drug-target interactions have become important in drug research because they can help to reduce the time, cost, and failure rates for developing new drugs. Recently, with the accumulation of drug-related data sets related to drug side effects and pharmacological data, it has became possible to predict potential drug-
Kim, Shinhyuk, Jin, Daeyong, Lee, Hyunju
openaire   +5 more sources

Algebraic shortcuts for leave-one-out cross-validation in supervised network inference [PDF]

, 2020
Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks.
Airola, Antti, De Baets, Bernard, Pahikkala, Tapio, Stock, Michiel, Waegeman, Willem   +4 more
core   +1 more source