Deep learning-based transcriptome data classification for drug-target interaction prediction
L Xie, S He, X Song, X Bo, Z Zhang - BMC genomics, 2018 - Springer
Background The ability to predict the interaction of drugs with target proteins is essential to
research and development of drug. However, the traditional experimental paradigm is costly …
research and development of drug. However, the traditional experimental paradigm is costly …
Deep learning in drug target interaction prediction: current and future perspectives
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery.
Computational methods in DTIs prediction have gained more attention because carrying out …
Computational methods in DTIs prediction have gained more attention because carrying out …
Machine learning for drug-target interaction prediction
Identifying drug-target interactions will greatly narrow down the scope of search of candidate
medications, and thus can serve as the vital first step in drug discovery. Considering that in …
medications, and thus can serve as the vital first step in drug discovery. Considering that in …
DeepPurpose: a deep learning library for drug–target interaction prediction
Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently,
deep learning (DL) models for show promising performance for DTI prediction. However …
deep learning (DL) models for show promising performance for DTI prediction. However …
Padme: A deep learning-based framework for drug-target interaction prediction
In silico drug-target interaction (DTI) prediction is an important and challenging problem in
biomedical research with a huge potential benefit to the pharmaceutical industry and …
biomedical research with a huge potential benefit to the pharmaceutical industry and …
Revealing drug-target interactions with computational models and algorithms
Background: Identifying possible drug-target interactions (DTIs) has become an important
task in drug research and development. Although high-throughput screening is becoming …
task in drug research and development. Although high-throughput screening is becoming …
Novel deep learning-based transcriptome data analysis for drug-drug interaction prediction with an application in diabetes
Q Luo, S Mo, Y Xue, X Zhang, Y Gu, L Wu, J Zhang… - BMC …, 2021 - Springer
Background Drug-drug interaction (DDI) is a serious public health issue. The L1000
database of the LINCS project has collected millions of genome-wide expressions induced …
database of the LINCS project has collected millions of genome-wide expressions induced …
HIDTI: integration of heterogeneous information to predict drug-target interactions
Identification of drug-target interactions (DTIs) plays a crucial role in drug development.
Traditional laboratory-based DTI discovery is generally costly and time-consuming …
Traditional laboratory-based DTI discovery is generally costly and time-consuming …
Open-source chemogenomic data-driven algorithms for predicting drug–target interactions
M Hao, SH Bryant, Y Wang - Briefings in bioinformatics, 2019 - academic.oup.com
While novel technologies such as high-throughput screening have advanced together with
significant investment by pharmaceutical companies during the past decades, the success …
significant investment by pharmaceutical companies during the past decades, the success …
Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data
Abstract Background Drug-drug interactions (DDIs) are one of the major concerns in drug
discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions …
discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions …