TransDTI: Transformer-Based Language Models for Estimating DTIs and Building a Drug Recommendation Workflow
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
TransDTI: Transformer-Based Language Models for Estimating DTIs and Building a Drug Recommendation Workflow
Authors
Keywords
-
Journal
ACS Omega
Volume 7, Issue 3, Pages 2706-2717
Publisher
American Chemical Society (ACS)
Online
2022-01-13
DOI
10.1021/acsomega.1c05203
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Improved protein structure prediction using potentials from deep learning
- (2020) Andrew W. Senior et al. NATURE
- SPVec: A Word2vec-Inspired Feature Representation Method for Drug-Target Interaction Prediction
- (2020) Yu-Fang Zhang et al. Frontiers in Chemistry
- TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
- (2020) Lifan Chen et al. BIOINFORMATICS
- Probable Pangolin Origin of SARS-CoV-2 Associated with the COVID-19 Outbreak
- (2020) Tao Zhang et al. CURRENT BIOLOGY
- Withanone and withaferin-A are predicted to interact with transmembrane protease serine 2 (TMPRSS2) and block entry of SARS-CoV-2 into cells
- (2020) Vipul Kumar et al. JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
- Editorial: In silico Methods for Drug Design and Discovery
- (2020) Simone Brogi et al. Frontiers in Chemistry
- MolTrans: Molecular interaction transformer for drug target interaction prediction
- (2020) Kexin Huang et al. BIOINFORMATICS
- DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method
- (2020) Yanyi Chu et al. BRIEFINGS IN BIOINFORMATICS
- DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
- (2019) Ingoo Lee et al. PLoS Computational Biology
- DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features
- (2019) Yanyi Chu et al. BRIEFINGS IN BIOINFORMATICS
- OUP accepted manuscript
- (2018) BIOINFORMATICS
- The Pfam protein families database in 2019
- (2018) Sara El-Gebali et al. NUCLEIC ACIDS RESEARCH
- Correction to When Does Chemical Elaboration Induce a Ligand To Change Its Binding Mode?
- (2017) Shipra Malhotra et al. JOURNAL OF MEDICINAL CHEMISTRY
- Deep-Learning-Based Drug–Target Interaction Prediction
- (2017) Ming Wen et al. JOURNAL OF PROTEOME RESEARCH
- Crystal structures of apo and inhibitor-bound TGFβR2 kinase domain: insights into TGFβR isoform selectivity
- (2016) Andrew J. Tebben et al. Acta Crystallographica Section D-Structural Biology
- Mapping the Pareto Optimal Design Space for a Functionally Deimmunized Biotherapeutic Candidate
- (2015) Regina S. Salvat et al. PLoS Computational Biology
- Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis
- (2014) Jing Tang et al. Journal of Chemical Information and Modeling
- Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments
- (2013) G. Madhavi Sastry et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution
- (2010) Jeremy R. Greenwood et al. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
- Protein-ligand interaction prediction: an improved chemogenomics approach
- (2008) Laurent Jacob et al. BIOINFORMATICS
- Prediction of drug-target interaction networks from the integration of chemical and genomic spaces
- (2008) Y. Yamanishi et al. BIOINFORMATICS
- Computational toxicology in drug development
- (2008) Wolfgang Muster et al. DRUG DISCOVERY TODAY
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now