Research graph
References from VCCV: conservative transcriptomic corroboration for measurement prioritization of computational drug–target hypotheses. Local targets link to admitted publications; unresolved targets remain external evidence.
Polypharmacology: challenges and opportunities in drug discovery: miniperspective
10.1021/jm5006463 · 2014 · External reference
Drug target prediction through deep learning functional representation of gene signatures
10.1038/s41467-024-46089-y · 2024 · External reference
scGPT: toward building a foundation model for single-cell multi-omics using generative AI
10.1038/s41592-024-02201-0 · 2024 · External reference
Comprehensive analysis of kinase inhibitor selectivity
10.1038/nbt.1990 · 2011 · External reference
MolTrans: molecular interaction transformer for drug–target interaction prediction
10.1093/bioinformatics/btaa880 · 2021 · External reference
Target deconvolution from phenotype-based drug discovery by using chemical proteomics approaches
10.1016/j.bbapap.2018.08.002 · 2019 · External reference
The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease
10.1126/science.1132939 · 2006 · External reference
Predicting emerging drug interactions using GNNs
10.1038/s43588-023-00555-7 · 2023 · External reference
Specific gene module pair-based target identification and drug discovery
10.3389/fphar.2022.1089217 · 2023 · External reference
SP-DTI: subpocket-informed transformer for drug–target interaction prediction
10.1093/bioinformatics/btaf011 · 2025 · External reference
DTIAM: a unified framework for predicting drug–target interactions, binding affinities and drug mechanisms
10.1038/s41467-025-57828-0 · 2025 · External reference
An analysis of approximations for maximizing submodular set functions, I
10.1007/bf01588971 · 1978 · External reference
GraphDTA: predicting drug–target binding affinity with graph neural networks
10.1093/bioinformatics/btaa921 · 2021 · External reference
Improving generalizability of drug–target binding prediction by pre-trained multi-view molecular representations
10.1093/bioinformatics/btaf002 · 2025 · External reference
DeepDTA: deep drug–target binding affinity prediction
10.1093/bioinformatics/bty593 · 2018 · External reference
10.7551/mitpress/1113.003.0008
10.7551/mitpress/1113.003.0008 · 2000 · External reference
Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery
10.1038/s41467-024-53457-1 · 2024 · External reference
Target identification and mechanism of action in chemical biology and drug discovery
10.1038/nchembio.1199 · 2013 · External reference
DeepDTAGen: a multitask deep learning framework for drug–target affinity prediction and target-aware drugs generation
10.1038/s41467-025-59917-6 · 2025 · External reference
LINCS Data Portal 2.0: next generation access point for perturbation-response signatures
10.1093/nar/gkz1023 · 2020 · External reference
A next generation connectivity map: l 1000 platform and the first 1,000,000 profiles
10.1016/j.cell.2017.10.049 · 2017 · External reference
How were new medicines discovered?
10.1038/nrd3480 · 2011 · External reference
Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis
10.1021/ci400709d · 2014 · External reference
Transfer learning enables predictions in network biology
10.1038/s41586-023-06139-9 · 2023 · External reference
MLG2Net: molecular global graph network for drug response prediction in lung cancer cell lines
10.1007/s10916-025-02182-3 · 2025 · External reference
PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations
10.1038/s44320-025-00131-3 · 2025 · External reference
GS-DTI: a graph-structure-aware framework leveraging large language models for drug–target interaction prediction
10.1093/bioinformatics/btaf445 · 2025 · External reference
Evidential deep learning-based drug–target interaction prediction
10.1038/s41467-025-62235-6 · 2025 · External reference
