The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
PubChem 2023 update
Open accessAbstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the ‘standardize’ option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Mathematical modeling of multi-objective decision-making has been prevalent since the 1960s (99).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [99]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Database resources of the National Center for Biotechnology Information: update
Wheeler, D. L. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh073 · Cited by 10,695
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436
UniProt: the universal protein knowledgebase in 2021
The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372
Database resources of the National Center for Biotechnology Information
Agarwala, R., Barrett, T., Beck, J., Benson, D., Bollin, C., Bolton, E. E., Bourexis, D., Brister, J., Bryant, S. H., Canese, K., et al. (2017) · Nucleic Acids Res. · doi:10.1093/nar/gkx1095 · Cited by 6,277
Self-citationPubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436
Self-citationPubChem in 2021: new data content and improved web interfaces
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa971 · Cited by 3,953
Self-citationThe Gene Ontology resource: enriching a GOld mine
The Gene Ontology Consortium, Carbon, S., Douglass, E., Good, B. M., Unni, D. R., Harris, N. L., Mungall, C. J., Basu, S., Chisholm, R. L., Dodson, R. J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1113 · Cited by 3,555
Self-citationPubChem 2019 update: improved access to chemical data
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249
Self-citationNCBI Taxonomy: a comprehensive update on curation, resources and tools
Schoch, C. L., Ciufo, S., Domrachev, M., Hotton, C. L., Kannan, S., Khovanskaya, R., Leipe, D., Mcveigh, R., O’Neill, K., Robbertse, B., et al. (2020) · Database · doi:10.1093/database/baaa062 · Cited by 2,635
Cited by
Sorted by citations
Kim, S., Chen, J., Cheng, T., Gindulytė, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1059 · Cited by 1,432
Self-citationFu, L., Shi, S., Yi, J., Wang, N., He, Y., Wu, Z., Peng, J., Deng, Y., Wang, W., Wu, C., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae236 · Cited by 1,396
Boltz-2: Towards Accurate and Efficient Binding Affinity PredictionPreprint
Passaro, S., Corso, G., Wohlwend, J., Reveiz, M., Thaler, S., Somnath, V. R., Getz, N., Portnoi, T., Roy, J., Stark, H., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.14.659707 · Cited by 940
Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
TTD: Therapeutic Target Database describing target druggability information
Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., Wang, S., Qiu, Y., Chen, Y., & Zhu, F. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad751 · Cited by 683
Database resources of the National Center for Biotechnology Information in 2025
Sayers, E. W., Beck, J. D., Bolton, E., Brister, J. R., Chan, J., Connor, R. P., Feldgarden, M., Fine, A. M., Funk, K., Hoffman, J., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae979 · Cited by 590
Self-citationCannon, M. J., Stevenson, J., Stahl, K., Basu, R., Coffman, A. C., Kiwala, S., McMichael, J. F., Kuzma, K., Morrissey, D., Cotto, K. C., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1040 · Cited by 540
FlyBase: updates to the Drosophila genes and genomes database
Öztürk‐Çolak, A., Marygold, S. J., Antonazzo, G., Attrill, H. L., Goutte-Gattat, D., Jenkins, V. K., Matthews, B. B., Millburn, G., dos Santos, G., Tabone, C. J., et al. (2024) · Genetics · doi:10.1093/genetics/iyad211 · Cited by 417
Database resources of the National Center for Biotechnology Information
Sayers, E. W., Beck, J. D., Bolton, E., Brister, J. R., Chan, J., Comeau, D. C., Connor, R. P., DiCuccio, M. N., Farrell, C. M., Feldgarden, M., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1044 · Cited by 400
Self-citationPer- and Polyfluoroalkyl Substances (PFAS) in PubChem: 7 Million and Growing
Schymanski, E., Zhang, J., Thiessen, P. A., Chirsir, P., Kondić, T., & Bolton, E. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c04855 · Cited by 371
Self-citationLIPID MAPS: update to databases and tools for the lipidomics community
Conroy, M. J., Andrews, R., Andrews, S., Cockayne, L., Dennis, E. A., Fahy, E., Gaud, C., Griffiths, W. J., Jukes, G., Kolchin, M., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad896 · Cited by 335
Reinvent 4: Modern AI–driven generative molecule design
Loeffler, H. H., He, J., Tibo, A., Janet, J. P., Voronov, A., Mervin, L., & Engkvist, O. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00812-5 · Cited by 440
Self-citationNORMAN guidance on suspect and non-target screening in environmental monitoring
Hollender, J., Schymanski, E., Ahrens, L., Alygizakis, ., Béen, F. M., Bijlsma, L., Brunner, A. M., Celma, A., Fildier, A., Fu, Q., et al. (2023) · Environmental Sciences Europe · doi:10.1186/s12302-023-00779-4 · Cited by 321
Network pharmacology: a crucial approach in traditional Chinese medicine research
Zhai, Y., Liu, L., Zhang, F., Chen, X., Wang, H., Zhou, J., Chai, K., Liu, J., Lei, H., Lu, P., et al. (2025) · Chinese Medicine · doi:10.1186/s13020-024-01056-z · Cited by 315
A review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274
GPCR drug discovery: new agents, targets and indications
Lorente, J. S., Sokolov, A. V., Ferguson, G., Schiöth, H. B., Hauser, A. S., & Gloriam, D. E. I. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01139-y · Cited by 306
Yang, R., Gao, W., Wang, Z., Jian, H., Peng, L., Yu, X., Xue, P., Peng, W., Li, K., & Zeng, P. (2023) · Phytomedicine · doi:10.1016/j.phymed.2023.155135 · Cited by 261
BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data
Liu, T., Hwang, L., Burley, S. K., Nitsche, C. I., Southan, C., Walters, W. P., & Gilson, M. K. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1075 · Cited by 282
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236
The IUPHAR/BPS Guide to PHARMACOLOGY in 2024
Harding, S. D., Armstrong, J. F., Faccenda, E., Southan, C., Alexander, S. P. H., Davenport, A. P., Spedding, M., & Davies, J. A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad944 · Cited by 226
Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. (2023) · Digital Discovery · doi:10.1039/d3dd00113j · Cited by 218
Ruan, T., Li, P., Wang, H., Li, T., & Jiang, G. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00056 · Cited by 224
Anstine, D. M., Zubatyuk, R. I., & Isayev, O. (2025) · Chemical Science · doi:10.1039/d4sc08572h · Cited by 231
Han, R., Yoon, H., Kim, G., Lee, H., & Lee, Y. (2023) · Pharmaceuticals · doi:10.3390/ph16091259 · Cited by 198
Nainala, V. C., Rajan, K., Kanakam, S. R. S., Sharma, N., Weißenborn, V., Schaub, J., & Steinbeck, C. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1063 · Cited by 169
Gangwal, A., & Lavecchia, A. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01257 · Cited by 167
RetractedZhang, S., Makoś, M. Z., Jadrich, R. B., Kraka, E., Barros, K. M., Nebgen, B. T., Tretiak, S., Isayev, O., Lubbers, N., Messerly, R. A., et al. (2024) · Nature Chemistry · doi:10.1038/s41557-023-01427-3 · Cited by 147
El‐Saadony, M. T., Saad, A. M., Mohammed, D. M., Alkafaas, S. S., Ghosh, S. B., Negm, S. H., Salem, H. M., Fahmy, M. A., Mosa, W. F. A., Ibrahim, E. H., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1603018 · Cited by 155
Amorim, A. M. B., Piochi, L. F., Gaspar, A. T., Preto, A. J., Rosário-Ferreira, N., & Moreira, I. S. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00352 · Cited by 132
High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage
Lai, Y., Koelmel, J. P., Walker, D. I., Price, E. J., Papazian, S., Manz, K. E., Castilla-Fernández, D., Bowden, J. A., Nikiforov, V. A., David, A., et al. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c01156 · Cited by 137
Cheng, M., Li, M., Zhang, Y., Gu, X., Gao, W., Zhang, S., & Liu, J. (2025) · Environment International · doi:10.1016/j.envint.2025.109324 · Cited by 127
Reviewing the Regulators of COL1A1
Devos, H., Zoidakis, J., Roubelakis, M. G., Latosinska, A., & Vlahou, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241210004 · Cited by 119
Foundation models in bioinformatics
Guo, F., Guan, R., Li, Y., Liu, Q., Wang, X., Yang, C., & Wang, J. (2025) · National Science Review · doi:10.1093/nsr/nwaf028 · Cited by 127
Zou, F., Cui, Z., Lou, S., Ou, Y., Zhu, C., Shu, C., Chen, J., Zhao, R., Wu, Z., Wang, L., et al. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1338902 · Cited by 112
Self-citationLysine lactylation in the regulation of tumor biology
Yang, Z., Zheng, Y., & Gao, Q. (2024) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2024.01.011 · Cited by 112
An Overview of Potential Alternatives for the Multiple Uses of Per- and Polyfluoroalkyl Substances
Figuière, R., Miaz, L. T., Savvidou, E. K., & Cousins, I. T. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.4c09088 · Cited by 117
Should Transformation Products Change the Way We Manage Chemicals?
Zahn, D., Arp, H. P. H., Fenner, K., Georgi, A., Hafner, J., Hale, S. E., Hollender, J., Letzel, T., Schymanski, E., Sigmund, G., et al. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c00125 · Cited by 115
A deep learning model for predicting selected organic molecular spectra
Zou, Z., Zhang, Y., Liang, L., Wei, M., Leng, J., Jiang, J., Luo, Y., & Hu, W. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00550-y · Cited by 106
Oh, M., Jang, S. Y., Lee, J.-Y., Kim, J. W., Jung, Y., Kim, J., Seo, J., Han, T., Jang, E., Son, H. Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41462-9 · Cited by 112
Self-citationTransformer-based models for chemical SMILES representation: A comprehensive literature review
Mswahili, M. E., & Jeong, Y.-S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39038 · Cited by 123
Turón, G., Hlozek, J., Woodland, J. G., Kumar, A., Chibale, K., & Duran‐Frigola, M. (2023) · Nature Communications · doi:10.1038/s41467-023-41512-2 · Cited by 99
Binding and sensing diverse small molecules using shape-complementary pseudocycles
An, L., Said, M. Y., Tran, L., Majumder, S., Goreshnik, I., Lee, G. R., Juergens, D., Dauparas, J., Anishchenko, I. V., Coventry, B., et al. (2024) · Science · doi:10.1126/science.adn3780 · Cited by 113
Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm
Xie, C., Qiu, H., Liu, L., You, Y., Li, H., Li, Y., Sun, Z., Lin, J., & An, L. (2025) · SmartMat · doi:10.1002/smm2.1320 · Cited by 96
Liao, X.-L., Chen, Z., Ou, S.-P., Liu, Q.-Y., Lin, S.-H., Zhou, J.-M., Wang, Y., & Cai, Z. (2023) · 中国科学通报：英文版 · doi:10.1016/j.scib.2023.12.045 · Cited by 91
Zhou, F. J., Peterson, T., Fan, Z., & Wang, S. F. (2023) · Nutrients · doi:10.3390/nu15183881 · Cited by 87
Herrera, L. P. T., Andreassen, S. N., Caroli, J., Rodríguez‐Espigares, I., Kermani, A. A., Keserű, G. M., Kooistra, A. J., Pándy‐Szekeres, G., & Gloriam, D. E. I. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1065 · Cited by 88
TamGen: drug design with target-aware molecule generation through a chemical language model
Wu, K., Xia, Y., Deng, P., Liu, R., Zhang, Y., Guo, H. J., Cui, Y., Pei, Q., Wu, L., Xie, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53632-4 · Cited by 103
SELFormer: molecular representation learning via SELFIES language models
Yüksel, A., Ulusoy, E., Ünlü, A., & Doğan, T. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acdb30 · Cited by 103
Mahto, M. K., Samanta, D., Shaw, M., Shaik, M. A. S., Basu, R., Mondal, I., Bhattacharya, A., & Pathak, A. (2023) · ACS Applied Nano Materials · doi:10.1021/acsanm.3c01523 · Cited by 77
ADCdb: the database of antibody–drug conjugates
Shen, L., Sun, X., Chen, Z., Guo, Y., Shen, Z., Song, Y., Xin, W., Ding, H., Ma, X., Xu, W., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad831 · Cited by 81
Pertpy: an end-to-end framework for perturbation analysis
Heumos, L., Ji, Y., May, L. T., Green, T. D., Peidli, S., Zhang, X., Wu, X., Ostner, J., Schumacher, A., Hrovatin, K., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02909-7 · Cited by 85
Macedo, B., Ribeiro‐Vaz, I., & Taveira‐Gomes, T. (2024) · Scientific Reports · doi:10.1038/s41598-023-50834-6 · Cited by 79
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
Bai, C., Wu, L., Li, R., Cao, Y., He, S., & Bo, X. (2025) · Advanced Science · doi:10.1002/advs.202413405 · Cited by 88
Self-citationFifteen years of ChEMBL and its role in cheminformatics and drug discovery
Zdrazil, B. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00963-z · Cited by 72
Application of Transformers in Cheminformatics
Luong, K.-D., & Singh, A. K. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c02070 · Cited by 78
Shimakawa, H., Kumada, A., & Sato, M. (2024) · npj Computational Materials · doi:10.1038/s41524-023-01194-2 · Cited by 81
A benchmark dataset for machine learning in ecotoxicology
Schür, C., Gasser, L., Pérez‐Cruz, F., Schirmer, K., & Baity‐Jesi, M. (2023) · Scientific Data · doi:10.1038/s41597-023-02612-2 · Cited by 75
Li, G., Zhao, B.-W., Su, X., Yang, Y., Hu, P., Zhou, X., & Hu, L. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3357979 · Cited by 69
Ge, B., Yan, K., Sang, R., Wang, W., Liu, X., Yu, M., Liu, X., Qiu, Q., & Zhang, X. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.116278 · Cited by 66
BitterDB: 2024 update on bitter ligands and taste receptors
Ziaikin, E., David, M., Uspenskaya, S., & Niv, M. Y. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1044 · Cited by 73
Nakata, M., & Maeda, T. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00899 · Cited by 79
Ultraselective Macrocycle Membranes for Pharmaceutical Ingredients Separation in Organic Solvents
Alhazmi, B., Ignácz, G., Vincenzo, M. D., Hedhili, M. N., Székely, G., & Nunes, S. P. (2024) · Nature Communications · doi:10.1038/s41467-024-51548-7 · Cited by 65
Microglial NLRP3 inflammasome-mediated neuroinflammation and therapeutic strategies in depression
Han, Q.-Q., Li, W., Chen, P., Wang, L., Bao, X., Huang, R., Liu, G., & Chen, X. (2023) · Neural Regeneration Research · doi:10.4103/1673-5374.390964 · Cited by 67
Zebosi, B., Vollbrecht, E., & Best, N. B. (2024) · Plant Communications · doi:10.1016/j.xplc.2024.100982 · Cited by 64
Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years
Sultan, A., Sieg, J., Mathea, M., & Volkamer, A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00747 · Cited by 78
DTI-LM: language model powered drug–target interaction prediction
Ahmed, K. T., Ansari, M. I., & Zhang, W. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae533 · Cited by 65
Leveraging infrared spectroscopy for automated structure elucidation
Alberts, M., Laino, T., & Vaucher, A. C. (2024) · Communications Chemistry · doi:10.1038/s42004-024-01341-w · Cited by 63
Merecz-Sadowska, A., Sadowski, A., Zielińska‐Bliźniewska, H., Zajdel, K., & Zajdel, R. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146678 · Cited by 70
P450Rdb: A manually curated database of reactions catalyzed by cytochrome P450 enzymes
Zhang, Y., Pan, X., Shi, T., Gu, Z.-F., Yang, Z., Liu, M., Xu, Y. M., Yang, Y., Ren, L., Song, X., et al. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.10.012 · Cited by 61
Structural insights into tecovirimat antiviral activity and poxvirus resistance
Vernuccio, R., León, A. M., Poojari, C., Buchrieser, J., Selverian, C. N., Jaleta, Y., Meola, A., Guivel‐Benhassine, F., Porrot, F., Haouz, A., et al. (2025) · Nature Microbiology · doi:10.1038/s41564-025-01936-6 · Cited by 63
Han, Y., Kim, D. H., & Pack, S. P. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262211121 · Cited by 68
Designing interphases for highly reversible aqueous zinc batteries
Pastel, G., Pollard, T. P., Liu, Q., Lavan, S. N., Zhu, Q., Jiang, R., Ma, L., Connell, J. G., Borodin, O. A., Schroeder, M. A., et al. (2024) · Joule · doi:10.1016/j.joule.2024.02.002 · Cited by 60
Accelerating battery innovation: AI-powered molecular discovery
Gao, Y., Chen, X., Yuan, Y., Chen, Y., Niu, Y.-L., Yao, N., Gao, Y.-B., Li, W. Z., & Zhang, Q. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00053j · Cited by 63
Development of PROTACs using computational approaches
Ge, J., Hsieh, C., Fang, M., Sun, H., & Hou, T. (2024) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2024.10.006 · Cited by 68
Understanding Quorum-Sensing and Biofilm Forming in Anaerobic Bacterial Communities
Markowska, K., Szymanek-Majchrzak, K., Pituch, H. M., & Majewska, A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252312808 · Cited by 59
Lalis, M., Hladiš, M., Khalil, S. A., Briand, L. V., Fiorucci, S., & Topin, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad886 · Cited by 58
Cui, Z., Cheng, F., Wang, L., Zou, F., Pan, R., Tian, Y., Zhang, X., She, J., Zhang, Y., & Yang, X. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1259908 · Cited by 58
MolPROP: Molecular Property prediction with multimodal language and graph fusion
Rollins, Z. A., Cheng, A. C., & Metwally, E. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00846-9 · Cited by 71
Koirala, M., Yan, L., Mohamed, Z., & DiPaola, M. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199384 · Cited by 78
Abchır, O., Daouı, O., Nour, H., Yamari, I., Elkhattabi, S., Errougui, A., & Chtita, S. (2023) · Scientific African · doi:10.1016/j.sciaf.2023.e01745 · Cited by 57
Benchmarking AI-powered docking methods from the perspective of virtual screening
Gu, S., Shen, C., Zhang, X., Sun, H., Cai, H., Luo, H., Zhao, H., Liu, B., Du, H., Zhao, Y., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00993-0 · Cited by 57
Cancer chemoprevention: signaling pathways and strategic approaches
Ren, J., Yan, G., Yang, L., Kong, L., Guan, Y., Sun, H., Liu, C., Liu, L., Han, Y., & Wang, X. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02167-1 · Cited by 67
Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions
Zhang, R., Wen, H., Lin, Z., Li, B., & Zhou, X. (2025) · Toxics · doi:10.3390/toxics13070525 · Cited by 56
Avoiding the Next Silent Spring: Our Chemical Past, Present, and Future
Arp, H. P. H., Aurich, D., Schymanski, E., Sims, K., & Hale, S. E. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c01735 · Cited by 58
Machine learning-assisted structure annotation of natural products based on MS and NMR data
Hu, G., & Qiu, M. (2023) · Natural Product Reports · doi:10.1039/d3np00025g · Cited by 55
Machine-learned molecular mechanics force fields from large-scale quantum chemical data
Takaba, K., Friedman, A. J., Cavender, C. E., Behara, P. K., Pulido, I., Henry, M. M., MacDermott-Opeskin, H., Iacovella, C. R., Nagle, A., Payne, A. M., et al. (2024) · Chemical Science · doi:10.1039/d4sc00690a · Cited by 59
Nooruzzaman, M., Johnson, K., Rani, R., Finkelsztein, E. J., Caserta, L. C., Kodiyanplakkal, R. P., Wang, W., Hsu, J., Salpietro, M. T., Banakis, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51924-3 · Cited by 57
Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
Mroz, A. M., Basford, A. R., Hastedt, F., Jayasekera, I. S., Mosquera‐Lois, I., Sedgwick, R., Ballester, P. J., Bocarsly, J. D., del Rio‐Chanona, E. A., Evans, M. L., et al. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00146c · Cited by 61
Molecular Docking in Drug Discovery: Techniques, Applications, and Advancements
Aguiar, C., & Camps, I. (2024) · Current Medicinal Chemistry · doi:10.2174/0109298673325827240926081845 · Cited by 61
Gong, C., Fu, X., Ma, Q., He, M., Zhu, X., Liu, L., Zhou, D., & Yan, S. (2024) · Phytomedicine · doi:10.1016/j.phymed.2024.156331 · Cited by 51
Novel Antimicrobial Agents for Gram-Negative Pathogens
Karvouniaris, M., Almyroudi, M. P., Abdul-Aziz, M. H., Blot, S. I., Paramythiotou, E., Tsigou, E., & Koulenti, D. (2023) · Antibiotics · doi:10.3390/antibiotics12040761 · Cited by 50
Zhou, M., Taiwo, K. J., Wang, H., Ntihuga, J. N., Angenent, L. T., & Usack, J. G. (2024) · Bioresources and Bioprocessing · doi:10.1186/s40643-024-00756-6 · Cited by 51
Bédard, C., Gagnon‐Arsenault, I., Boisvert, J., Plante, S., Dubé, A. K., Pageau, A., Fijarczyk, A., Sharma, J., Maroc, L., Shapiro, R. S., et al. (2024) · Nature Microbiology · doi:10.1038/s41564-024-01819-2 · Cited by 50
PCP-SAFT Parameters of Pure Substances Using Large Experimental Databases
Esper, T., Bauer, G., Rehner, P., & Groß, J. (2023) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.3c02255 · Cited by 49
THPdb2: compilation of FDA approved therapeutic peptides and proteins
Jain, S., Gupta, S., Patiyal, S., & Raghava, G. P. S. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104047 · Cited by 48
Hesperetin Alleviated Experimental Colitis via Regulating Ferroptosis and Gut Microbiota
Wang, J., Yao, Y., Yao, T. S., Shi, Q., Zeng, Y., & Li, L. (2024) · Nutrients · doi:10.3390/nu16142343 · Cited by 48
mVOC 4.0: a database of microbial volatiles
Kemmler, E., Lemfack, M. C., Goede, A., Gallo, K., Toguem, S. M. T., Ahmed, W., Millberg, I., Preißner, S., Piechulla, B., & Preißner, R. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae961 · Cited by 48
Advances and Challenges in Machine Learning for RNA-Small Molecule Interaction Modeling: Review
Sun, T., Xia, W., Shu, J., Sang, C., Feng, M., & Xu, X. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00973 · Cited by 48
Zhu, M., He, Q., Wang, Y., Duan, L., Rong, K., Wu, Y., Ding, Y., Mi, Y., Ge, X., Yang, X., et al. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1238841 · Cited by 48
BIOPEP-UWM database — present and future
Iwaniak, A., Mińkiewicz, P., & Darewicz, M. (2023) · Current Opinion in Food Science · doi:10.1016/j.cofs.2023.101108 · Cited by 54
Metadata
- Authors
- Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E.
- Affiliations
- National Institutes of Health (US)
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services , Bethesda , MD, 20894, USA
- Corresponding author
- Bolton, Evan E
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-10-28
- Volume
- 51
- Issue
- D1
- Pages
- D1373-D1380
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC9825602
- OpenAlex
- W4307468223
- Cited by
- 3,443
- References
- 58
- Citations by year
- 2026: 811 · 2025: 1150 · 2024: 1094 · 2023: 379 · 2022: 2
- FWCI
- 356.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- other-oa
- Locations
- Nucleic Acids Research (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Center for Biotechnology Information of the National Library of Medicine
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- PubChem, chemical information resources, patent information
- MeSH terms
- Cheminformatics, Biological Assay, Proteins, Drug Discovery, Databases, Chemical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
