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Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences.
Dtigems+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
M. Thafar, R. Olayan, H. Ashoor, S. Albaradei, V. Bajic, X. Gao, T. Gojobori, and M. Essack · 1976
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Gene ontology: tool for the unification of biology
M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. E. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, and G. Sherlock · 2000
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Bindingdb: a web-accessible database of experimentally determined protein–ligand binding affinities
T. Liu, Y. Lin, X. Wen, R. N. Jorissen, and M. K. Gilson · 2007
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ChEMBL: a large-scale bioactivity database for drug discovery
A. Gaulton, L. J. Bellis, A. P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, and J. P. Overington · 2011
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Directory of useful decoys, enhanced (dud-e): better ligands and decoys for better benchmarking
M. M. Mysinger, M. Carchia, J. J. Irwin, and B. K. Shoichet · 2012
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DrugBank 4.0: shedding new light on drug metabolism
V. Law, C. Knox, Y. Djoumbou, T. Jewison, A. C. Guo, Y. Liu, A. Maciejewski, D. Arndt, M. Wilson, V. Neveu, A. Tang, G. Gabriel, C. Ly, S. Adamjee, Z. T. Dame, B. Han, Y. Zhou, and D. S. Wishart · 2013
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Stitch 5: augmenting protein-chemical interaction networks with tissue and affinity data
D. Szklarczyk, A. Santos, C. von Mering, L. J. Jensen, P. Bork, and M. Kuhn · 2016
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Deepdta: deep drug–target binding affinity prediction
H. Öztürk, A. Özgür, and E. Ozkirimli · 2018
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Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling · 2018
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Evaluating protein transfer learning with tape
R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, P. Chen, J. Canny, P. Abbeel, and Y. Song · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
N. Reimers and I. Gurevych · 2019
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. L. Zitnick, J. Ma, and R. Fergus · 2019
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A. Elnaggar, M. Heinzinger, C. Dallago, G. Rehawi, Y. Wang, L. Jones, T. Gibbs, T. Feher, C. Angerer, M. Steinegger, D. Bhowmik, and B. Rost · 2020
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Molecular representation learning with language models and domain-relevant auxiliary tasks
B. Fabian, T. Edlich, H. Gaspar, M. Segler, J. Meyers, M. Fiscato, and M. Ahmed · 2020
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Corpus processing service: A knowledge graph platform to perform deep data exploration on corpora
P. W. J. Staar, M. Dolfi, and C. Auer · 2020
Cited alongside, same era.
Graph neural networks: A review of methods and applications
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun · 2020
Cited alongside, same era.
Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings
M. Fey, J. E. Lenssen, F. Weichert, and J. Leskovec · 2021
Cited alongside, same era.
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
K. Huang, T. Fu, W. Gao, Y. Zhao, Y. Roohani, J. Leskovec, C. Coley, C. Xiao, J. Sun, and M. Zitnik · 2021
Cited alongside, same era.
Msa transformer
R. M. Rao, J. Liu, R. Verkuil, J. Meier, J. Canny, P. Abbeel, T. Sercu, and A. Rives · 2021
Cited alongside, same era.
The next-generation Open Targets Platform: reimagined, redesigned, rebuilt
D. Ochoa, A. Hercules, M. Carmona, D. Suveges, J. Baker, C. Malangone, I. Lopez, A. Miranda, C. Cruz-Castillo, L. Fumis, M. Bernal-Llinares, K. Tsukanov, H. Cornu, K. Tsirigos, O. Razuvayevskaya, A. Buniello, J. Schwartzentruber, M. Karim, B. Ariano, R. Martinez Osorio, J. Ferrer, X. Ge, S. Machlitt-Northen, A. Gonzalez-Uriarte, S. Saha, S. Tirunagari, C. Mehta, J. Roldán-Romero, S. Horswell, S. Young, M. Ghoussaini, D. Hulcoop, I. Dunham, and E. McDonagh · 2022
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Large-scale chemical language representations capture molecular structure and properties
J. Ross, B. Belgodere, V. Chenthamarakshan, I. Padhi, Y. Mroueh, and P. Das · 2022
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Molformer: Large scale chemical language representations capture molecular structure and properties
J. Ross, B. Belgodere, V. Chenthamarakshan, I. Padhi, Y. Mroueh, and P. Das · 2022
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Learning the drug-target interaction lexicon
R. Singh, S. Sledzieski, L. Cowen, and B. Berger · 2022
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Adapting protein language models for rapid dti prediction
S. Sledzieski, R. Singh, L. Cowen, and B. Berger · 2022
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A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. Zitnick, J. Ma, and R. Fergus · 2021
Cited alongside, same era.
Modeling protein using large-scale pretrain language model
Y. Xiao, J. Qiu, Z. Li, C. Hsieh, and J. Tang · 2021
Cited alongside, same era.
ProteinBERT: a universal deep-learning model of protein sequence and function
N. Brandes, D. Ofer, Y. Peleg, N. Rappoport, and M. Linial · 2022
Cited alongside, same era.
UniProt: the Universal Protein Knowledgebase in 2023
T. U. Consortium · 2022
Cited alongside, same era.
Artificial intelligence foundation for therapeutic science
K. Huang, T. Fu, W. Gao, Y. Zhao, Y. Roohani, J. Leskovec, C. W. Coley, C. Xiao, J. Sun, and M. Zitnik · 2022
Cited alongside, same era.
Graph representation learning in biomedicine and healthcare
M. M. Li, K. Huang, and M. Zitnik · 2022
Cited alongside, same era.
Language models of protein sequences at the scale of evolution enable accurate structure prediction
Z. Lin, H. Akin, R. Rao, B. Hie, Z. Zhu, W. Lu, N. Smetanin, A. dos Santos Costa, M. Fazel-Zarandi, T. Sercu, S. Candido, et al · 2022
Cited alongside, same era.
Later among the works it cites.
Ontoprotein: Protein pretraining with gene ontology embedding
N. Zhang, Z. Bi, X. Liang, S. Cheng, H. Hong, S. Deng, Q. Zhang, J. Lian, and H. Chen · 2022
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The gene ontology knowledgebase in 2023
G. Central, S. A. Aleksander, J. Balhoff, S. Carbon, J. M. Cherry, H. J. Drabkin, D. Ebert, M. Feuermann, P. Gaudet, N. L. Harris, et al · 2023
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Building a knowledge graph to enable precision medicine
P. Chandak, K. Huang, and M. Zitnik · 2023
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Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
K. Huang, P. Chandak, Q. Wang, S. Havaldar, A. Vaid, J. Leskovec, G. Nadkarni, B. Glicksberg, N. Gehlenborg, and M. Zitnik · 2023
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Smilesformer: Language model for molecular design
J. Owoyemi and N. Medzhidov · 2023
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The string database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest
D. Szklarczyk, R. Kirsch, M. Koutrouli, K. Nastou, F. Mehryary, R. Hachilif, A. Gable, T. Fang, N. Doncheva, S. Pyysalo, P. Bork, L. J. Jensen, and C. von Mering · 2023
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Selformer: Molecular representation learning via selfies language models
A. Yüksel, E. Ulusoy, A. Ünlü, G. Deniz, and T. Doğan · 2023
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Protein representation learning via knowledge enhanced primary structure reasoning
H.-Y. Zhou, Y. Fu, Z. Zhang, B. Cheng, and Y. Yu · 2023
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