Research graph
References from G– PLIP : Knowledge graph neural network for structure-free protein–ligand bioactivity prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
Molecular dynamics simulations of biomolecules
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10.1021/jm100112j · External reference
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10.1021/acs.jproteome.8b00341
10.1021/acs.jproteome.8b00341 · External reference
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10.1016/j.csbj.2021.08.011
10.1016/j.csbj.2021.08.011 · External reference
Computational representations of protein–ligand interfaces for structure-based virtual screening
10.1080/17460441.2021.1929921 · 2021 · External reference
Informatics and computational methods in natural product drug discovery: a review and perspectives
10.3389/fgene.2019.00368 · 2019 · External reference
The HADDOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes
2016 · External reference
Structural biology in the clouds: the WeNMR-EOSC ecosystem
10.3389/fmolb.2021.729513 · 2021 · External reference
On the frustration to predict binding affinities from protein–ligand structures with deep neural networks
10.1021/acs.jmedchem.2c00487 · 2022 · External reference
Development and evaluation of a deep learning model for protein–ligand binding affinity prediction
10.1093/bioinformatics/bty374 · 2018 · External reference
Improved protein–ligand binding affinity prediction with structure-based deep fusion inference
10.1021/acs.jcim.0c01306 · 2021 · External reference
KDEEP: protein–ligand absolute binding affinity prediction via 3D-convolutional neural networks
10.1021/acs.jcim.7b00650 · 2018 · External reference
A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs
10.1038/s41467-023-40064-9 · 2023 · External reference
Evaluation of local and global machine learning models for the prediction of ADME properties
10.1021/acs.molpharmaceut.2c00962 · 2023 · External reference
Toward quantitative models in safety assessment: a case study to show impact of dose–response inference on hERG inhibition models
10.3390/ijms24010635 · 2023 · External reference
Structural mechanism of a drug-binding process involving a large conformational change of the protein target
10.1038/s41467-023-36956-5 · 2023 · External reference
KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor
10.1093/bioinformatics/btp167 · 2009 · External reference
10.1080/17460441.2021.1909567
10.1080/17460441.2021.1909567 · External reference
10.1038/s42256-021-00418-8
10.1038/s42256-021-00418-8 · External reference
Structure-based drug design with geometric deep learning
10.1016/j.sbi.2023.102548 · 2023 · External reference
Unresolved reference
2022 · External reference
Unresolved reference
2021 · External reference
Unresolved reference
2022 · External reference
Δ-quantum machine-learning for medicinal chemistry
10.1039/d2cp00834c · 2022 · External reference
Machine learning for fast, quantum mechanics-based approximation of drug lipophilicity
10.1021/acsomega.2c05607 · 2023 · External reference
Unresolved reference
2021 · External reference
Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction
10.1186/s13321-019-0407-y · 2020 · External reference
Pre-training of graph neural network for modeling effects of mutations on protein-protein binding affinity
2021 · External reference
DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing
2022 · External reference
10.1109/msp.2017.2693418
10.1109/msp.2017.2693418 · External reference
Unresolved reference
2023 · External reference
Unresolved reference
2019 · External reference
EquiBind: geometric deep learning for drug binding structure prediction in proceedings of the 39th international conference on machine learning
2022 · External reference
Deep learning approaches for conformational flexibility and switching properties in protein design
2022 · External reference
Context-aware geometric deep learning for protein sequence design
2023 · External reference
Deep local analysis evaluates protein docking conformations with locally oriented cubes
10.1093/bioinformatics/btac551 · 2022 · External reference
Deep local analysis deconstructs protein–protein interfaces and accurately estimates binding affinity changes upon mutation
10.1093/bioinformatics/btad231 · 2023 · External reference
10.1038/s41592-019-0666-6
10.1038/s41592-019-0666-6 · External reference
Protein-protein interaction site prediction through combining local and global features with deep neural networks
10.1093/bioinformatics/btz699 · 2020 · External reference
Predicting protein–protein interactions through sequence-based deep learning
10.1093/bioinformatics/bty573 · 2018 · External reference
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
10.1038/s41467-023-37701-8 · 2023 · External reference
Topsy-Turvy: integrating a global view into sequence-based PPI prediction
10.1093/bioinformatics/btac258 · 2022 · External reference
Current advances and limitations of deep learning in anticancer drug sensitivity prediction
10.2174/1568026620666200710101307 · 2020 · External reference
10.1038/s41573-019-0024-5
10.1038/s41573-019-0024-5 · External reference
Modeling polypharmacy side effects with graph convolutional networks
10.1093/bioinformatics/bty294 · 2018 · External reference
Simple nearest-neighbour analysis meets the accuracy of compound potency predictions using complex machine learning models
2022 · External reference
Unresolved reference
2017 · External reference
10.1186/1471-2105-5-113
10.1186/1471-2105-5-113 · External reference
UniProt: the universal protein knowledgebase in 2023
10.1093/nar/gkac1052 · 2023 · External reference
10.1038/ng0304-214
10.1038/ng0304-214 · External reference
A single-cell type transcriptomics map of human tissues
10.1126/sciadv.abh2169 · 2021 · External reference
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
10.1016/s0169-409x(00)00129-0 · 2001 · External reference
Two decades under the influence of the rule of five and the changing properties of approved oral drugs
10.1021/acs.jmedchem.8b00686 · 2019 · External reference
A bidirectional permeability assay for beyond rule of 5 compounds
10.3390/pharmaceutics13081146 · 2021 · External reference
10.1021/ci990307l
10.1021/ci990307l · External reference
10.1021/ci100050t
10.1021/ci100050t · External reference
10.1021/acs.jcim.2c00695
10.1021/acs.jcim.2c00695 · External reference
Targeting HIV/HCV coinfection using a machine learning-based multiple quantitative structure-activity relationships (multiple QSAR) method
10.3390/ijms20143572 · 2019 · External reference
Unresolved reference
2015 · External reference
10.1126/science.ade2574
10.1126/science.ade2574 · External reference
On the use of the Pearson correlation coefficient for model evaluation in genome-wide prediction
10.3389/fgene.2019.00899 · 2019 · External reference
10.1016/j.semcancer.2020.02.003
10.1016/j.semcancer.2020.02.003 · External reference
A similarity measure for indefinite rankings
10.1145/1852102.1852106 · 2010 · External reference
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10.1073/pnas.1911532117 · External reference
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10.1021/jm9602928 · 1996 · External reference
The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures
10.1021/jm030580l · 2004 · External reference
10.1021/jm048957q
10.1021/jm048957q · External reference
Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning
10.1039/d3ra08650j · 2024 · External reference
Predicting or pretending: artificial intelligence for protein-ligand interactions lack of sufficiently large and unbiased datasets
2020 · External reference
DeepDTA: deep drug–target binding affinity prediction
10.1093/bioinformatics/bty593 · 2018 · External reference
Unresolved reference
External reference
10.1021/acs.jcim.1c01531
10.1021/acs.jcim.1c01531 · External reference
Conducting correlation analysis: important limitations and pitfalls
10.1093/ckj/sfab085 · 2021 · External reference
Low-quality structural and interaction data improves binding affinity prediction via random forest
10.3390/molecules200610947 · 2015 · External reference
Unresolved reference
2022 · External reference
Latent biases in machine learning models for predicting binding affinities using popular data sets
10.1021/acsomega.2c06781 · 2023 · External reference
10.1016/j.csbj.2014.08.008 · Admitted local publication
10.1038/s41586-020-2188-x
10.1038/s41586-020-2188-x · External reference
10.1038/s41588-023-01327-9
10.1038/s41588-023-01327-9 · External reference
10.1038/s42256-022-00580-7
10.1038/s42256-022-00580-7 · External reference
Protein language models trained on multiple sequence alignments learn phylogenetic relationships
10.1038/s41467-022-34032-y · 2022 · External reference
Generative power of a protein language model trained on multiple sequence alignments
10.7554/elife.79854 · 2023 · External reference
ProtTrans: toward understanding the language of life through self-supervised learning
10.1109/tpami.2021.3095381 · 2022 · External reference
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10.1016/b978-0-12-417205-0.00002-x · External reference
10.1016/j.vascn.2018.07.001
10.1016/j.vascn.2018.07.001 · External reference
Targeted degradation of transcription factors by TRAFTACs: TRAnscription factor TArgeting chimeras
2021 · External reference
Unresolved reference
2019 · External reference
Unresolved reference
2019 · External reference
