The DOI matches its Crossref record.
Open Babel: An open chemical toolbox
Open accessAbstract
BACKGROUND: A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats. RESULTS: We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion. CONCLUSIONS: Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org.
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.
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
Cited by
Daina, A., Michielin, O., & Zoete, V. (2017) · Scientific Reports · doi:10.1038/srep42717 · Cited by 18,367
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515
Atomsk: A tool for manipulating and converting atomic data files
Hirel, P. (2015) · Computer Physics Communications · doi:10.1016/j.cpc.2015.07.012 · Cited by 2,336
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966
Searching molecular structure databases with tandem mass spectra using CSI:FingerID
Dührkop, K., Shen, H., Meusel, M., Rousu, J., & Böcker, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509788112 · Cited by 1,190
Bayesian reaction optimization as a tool for chemical synthesis
Shields, B. J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J. I. M., Janey, J. M., Adams, R. P., & Doyle, A. G. (2021) · Nature · doi:10.1038/s41586-021-03213-y · Cited by 1,063
Proteome-wide covalent ligand discovery in native biological systems
Backus, K. M., Correia, B. E., Lum, K. M., Forli, S., Horning, B. D., González-Páez, G. E., Chatterjee, S., Lanning, B. R., Teijaro, J. R., Olson, A. J., et al. (2016) · Nature · doi:10.1038/nature18002 · Cited by 940
Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978
Macromolecular modeling and design in Rosetta: recent methods and frameworks
Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888
BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities
Minkiewicz, P., Iwaniak, A., & Darewicz, M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20235978 · Cited by 958
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911
Machine learning unifies the modeling of materials and molecules
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764
A structural biology community assessment of AlphaFold2 applications
Akdel, M., Pires, D. E. V., Pardo, E. P., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00849-w · Cited by 675
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651
Understanding the diversity of the metal-organic framework ecosystem
Moosavi, S. M., Nandy, A., Jablonka, K. M., Ongari, D., Janet, J. P., Boyd, P. G., Lee, Y., Smit, B., & Kulik, H. J. (2020) · Nature Communications · doi:10.1038/s41467-020-17755-8 · Cited by 622
Gentile, F., Yaacoub, J. C., Gleave, J., Fernandez, M., Ton, A.-T., Ban, F., Stern, A., & Cherkasov, A. (2022) · Nature Protocols · doi:10.1038/s41596-021-00659-2 · Cited by 397
Sun, W., Zheng, Y., Yang, K., Zhang, Q., Shah, A. A., Wu, Z., Sun, Y., Feng, L., Chen, D., Xiao, Z., et al. (2019) · Science Advances · doi:10.1126/sciadv.aay4275 · Cited by 344
Performance of machine-learning scoring functions in structure-based virtual screening.
Wójcikowski, M., Ballester, P. J., & Siedlecki, P. (2017) · Scientific Reports · doi:10.1038/srep46710 · Cited by 364
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358
Target identification among known drugs by deep learning from heterogeneous networks
Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., et al. (2020) · Chemical Science · doi:10.1039/c9sc04336e · Cited by 357
Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017) · Oncotarget · doi:10.18632/oncotarget.14073 · Cited by 350
AutoDock4Zn: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins
Santos-Martins, D., Forli, S., Ramos, M. J., & Olson, A. J. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500209e · Cited by 318
Döntgen, M., Przybylski-Freund, M.-D., Kröger, L. C., Kopp, W. A., Ismail, A. E., & Leonhard, K. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00201 · Cited by 308
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019) · Chemical Science · doi:10.1039/c9sc02298h · Cited by 243
Judson, R., Houck, K., Martin, M., Richard, A. M., Knudsen, T. B., Shah, I., Little, S., Wambaugh, J., Woodrow Setzer, R., Kothya, P., et al. (2016) · Toxicological Sciences · doi:10.1093/toxsci/kfw092 · Cited by 232
eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates
Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212
“Input data to machine learning models are 1024-bit Day-light fingerprints constructed for dataset compounds with Open Babel [61].”
MethodMachine learning meets volcano plots: computational discovery of cross-coupling catalysts
Meyer, B., Sawatlon, B., Heinen, S., von Lilienfeld, O. A., & Corminboeuf, C. (2018) · Chemical Science · doi:10.1039/c8sc01949e · Cited by 209
Bauer, M. R., Ibrahim, T. M., Vogel, S. M., & Boeckler, F. M. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400115b · Cited by 200
Janet, J. P., Ramesh, S., Duan, C., & Kulik, H. J. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00026 · Cited by 200
Predicting electronic structure properties of transition metal complexes with neural networks
Janet, J. P., & Kulik, H. J. (2017) · Chemical Science · doi:10.1039/c7sc01247k · Cited by 199
Marchese Robinson, R. L., Palczewska, A., Palczewski, J., & Kidley, N. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00753 · Cited by 167
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
Nandy, A., Duan, C., Janet, J. P., Gugler, S., & Kulik, H. J. (2018) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b04015 · Cited by 161
Nguyen, D. D., Cang, Z., Wu, K., Wang, M., Cao, Y., & Wei, G.-W. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0146-6 · Cited by 155
Rationalizing PROTAC-Mediated Ternary Complex Formation Using Rosetta
Bai, N., Miller, S. A., Andrianov, G. V., Yates, M., Kirubakaran, P., & Karanicolas, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01451 · Cited by 157
Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design
Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154
“Structure files were preprocessed to be in compatible file formats using AutoDockTools version 1.5.6 and Open Babel version 2.3.2 (O’Boyle et al., 2011).”
MethodInfluentialA structural biology community assessment of AlphaFold 2 applications
Akdel, M., Pires, D. E. V., Porta Pardo, E., Jänes, J., Zalevsky, A. O., Mészáros, B., Bryant, P., Good, L. L., Laskowski, R. A., Pozzati, G., et al. (2021) · doi:10.1101/2021.09.26.461876 · Cited by 122
Myint, K.-Z., Wang, L., Tong, Q., & Xie, X.-Q. (2012) · Molecular Pharmaceutics · doi:10.1021/mp300237z · Cited by 139
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136
HCK is a survival determinant transactivated by mutated MYD88, and a direct target of ibrutinib
Yang, G., Buhrlage, S. J., Tan, L., Liu, X., Chen, J., Xu, L., Tsakmaklis, N., Chen, J. G., Patterson, C. J., Brown, J. R., et al. (2016) · Blood · doi:10.1182/blood-2016-01-695098 · Cited by 135
Ouyang, X., Zhou, S., Su, C. T. T., Ge, Z., Li, R., & Kwoh, C. K. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23136 · Cited by 134
DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network
Pu, L., Govindaraj, R. G., Lemoine, J. M., Wu, H.-C., & Brylinski, M. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006718 · Cited by 133
Integrative Modeling of PROTAC-Mediated Ternary Complexes
Weng, G., Li, D., Kang, Y., & Hou, T. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01576 · Cited by 130
In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data
Celebi, R., Bear Don’t Walk, O., Movva, R., Alpsoy, S., & Dumontier, M. (2019) · Scientific Reports · doi:10.1038/s41598-019-45236-6 · Cited by 120
Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.9b00109 · Cited by 114
PocketFlow is a data-and-knowledge-driven structure-based molecular generative model
Jiang, Y., Zhang, G., You, J., Zhang, H., Yao, R., Xie, H., Zhang, L., Xia, Z., Dai, M., Wu, Y., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00808-8 · Cited by 97
Duan, C., Janet, J. P., Liu, F., Nandy, A., & Kulik, H. J. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00057 · Cited by 87
Haghighatlari, M., Vishwakarma, G., Altarawy, D., Subramanian, R., Kota, B. U., Sonpal, A., Setlur, S., & Hachmann, J. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1458 · Cited by 92
Hollands, A., Corriden, R., Gysler, G., Dahesh, S., Olson, J., Raza Ali, S., Kunkel, M. T., Lin, A. E., Forli, S., Newton, A. C., et al. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.695866 · Cited by 73
Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage
Cordova, M., Wodrich, M. D., Meyer, B., Sawatlon, B., & Corminboeuf, C. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c00774 · Cited by 68
Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction
Sadowski, P., Fooshee, D., Subrahmanya, N., & Baldi, P. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00351 · Cited by 65
iCn3D: From Web-Based 3D Viewer to Structural Analysis Tool in Batch Mode
Wang, J., Youkharibache, P., Marchler-Bauer, A., Lanczycki, C., Zhang, D., Lu, S., Madej, T., Marchler, G. H., Cheng, T., Chong, L. C., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.831740 · Cited by 65
Cyanobacteria as Photosynthetic Biocatalysts: A Systems Biology Perspective
Gudmundsson, S., & Nogales, J. (2014) · Molecular BioSystems · doi:10.1039/c4mb00335g · Cited by 61
Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design
Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b00808 · Cited by 56
Insights from incorporating quantum computing into drug design workflows
Lau, B., Emani, P. S., Chapman, J., Yao, L., Lam, T., Merrill, P., Warrell, J., Gerstein, M. B., & Lam, H. Y. K. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac789 · Cited by 54
Kushwaha, P. P., Singh, A. K., Prajapati, K. S., Shuaib, M., Gupta, S., & Kumar, S. (2021) · Microbial Pathogenesis · doi:10.1016/j.micpath.2021.104954 · Cited by 53
Paajanen, A., Rinta-Paavola, A., & Vaari, J. (2021) · Cellulose · doi:10.1007/s10570-021-04084-2 · Cited by 51
Sharma, S., Nehru, B., & Saini, A. (2017) · Neurochemistry International · doi:10.1016/j.neuint.2017.06.011 · Cited by 48
On-the-fly QM/MM docking with attracting cavities.
Chaskar, P., Zoete, V., & Röhrig, U. F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00406 · Cited by 46
Ding, B., Wang, J., Li, N., & Wang, W. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300508m · Cited by 45
Electrolytomics: A Unified Big Data Approach for Electrolyte Design and Discovery
Kumar, R., Vu, M. C., Ma, P., & Amanchukwu, C. V. (2025) · Chemistry of Materials · doi:10.1021/acs.chemmater.4c03196 · Cited by 45
Andersen, J., Andersen, T., Flamm, C., Hanczyc, M., Merkle, D., & Stadler, P. (2013) · Entropy · doi:10.3390/e15104066 · Cited by 43
EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling
Shao, Q., Jiang, Y., & Yang, Z. J. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01424 · Cited by 43
Enumeration of de novo inorganic complexes for chemical discovery and machine learning
Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/c9me00069k · Cited by 43
Guanosine monophosphate reductase 1 is a potential therapeutic target for Alzheimer’s disease
Liu, H., Luo, K., & Luo, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-21256-6 · Cited by 42
Bhagavat, R., Sankar, S., Srinivasan, N., & Chandra, N. (2018) · Structure · doi:10.1016/j.str.2018.02.001 · Cited by 41
Antagonistic interactions between odorants alter human odor perception
Fukutani, Y., Abe, M., Saito, H., Eguchi, R., Tazawa, T., de March, C. A., Yohda, M., & Matsunami, H. (2023) · Current Biology · doi:10.1016/j.cub.2023.04.072 · Cited by 41
Porté, S., Xavier Ruiz, F., Giménez, J., Molist, I., Alvarez, S., Domínguez, M., Alvarez, R., de Lera, A. R., Parés, X., & Farrés, J. (2013) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2012.11.014 · Cited by 38
Automated Building of Organometallic Complexes from 3D Fragments
Foscato, M., Venkatraman, V., Occhipinti, G., Alsberg, B. K., & Jensen, V. R. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5003153 · Cited by 35
FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening
Zhou, H., Cao, H., & Skolnick, J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01160 · Cited by 32
DiffDec: Structure-Aware Scaffold Decoration with an End-to-End Diffusion Model
Xie, J., Chen, S., Lei, J., & Yang, Y. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01466 · Cited by 31
Basis for Accurate Protein p K a Prediction with Machine Learning
Cai, Z., Liu, T., Lin, Q., He, J., Lei, X., Luo, F., & Huang, Y. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00254 · Cited by 31
Systemic evolutionary chemical space exploration for drug discovery
Lu, C., Liu, S., Shi, W., Yu, J., Zhou, Z., Zhang, X., Lu, X., Cai, F., Xia, N., & Wang, Y. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00598-4 · Cited by 30
Georgakopoulos, N., Talapatra, S., Dikovskaya, D., Dayalan Naidu, S., Higgins, M., Gatliff, J., Ayhan, A., Nikoloudaki, R., Schaap, M., Valko, K., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00457 · Cited by 33
Automatic and accurate ligand structure determination guided by cryo-electron microscopy maps
Muenks, A., Zepeda, S., Zhou, G., Veesler, D., & DiMaio, F. (2023) · Nature Communications · doi:10.1038/s41467-023-36732-5 · Cited by 28
Bayesian optimization for ternary complex prediction (BOTCP)
Rao, A., Tunjic, T. M., Brunsteiner, M., Müller, M., Fooladi, H., Gasbarri, C., & Weber, N. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100072 · Cited by 25
The chemical interactome space between the human host and the genetically defined gut metabotypes
Jacobsen, U. P., Nielsen, H. B., Hildebrand, F., Raes, J., Sicheritz-Ponten, T., Kouskoumvekaki, I., & Panagiotou, G. (2013) · The ISME Journal · doi:10.1038/ismej.2012.141 · Cited by 24
A mutation-induced drug resistance database (MdrDB)
Yang, Z., Ye, Z., Qiu, J., Feng, R., Li, D., Hsieh, C., Allcock, J., & Zhang, S. (2023) · Communications Chemistry · doi:10.1038/s42004-023-00920-7 · Cited by 24
Myint, K. Z., & Xie, X.-Q. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2239-0_9 · Cited by 21
Villard, J., Kılıç, M., & Rothlisberger, U. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01078 · Cited by 19
Baev, A. Y., Charishnikova, O. S., Khasanov, F. A., Nebesnaya, K. S., Makhmudov, A. R., Rakhmedova, M. T., Khushbaktova, Z. A., Syrov, V. N., & Levitskaya, Y. V. (2022) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/j.jsbmb.2022.106066 · Cited by 16
How accurately can one predict drug binding modes using AlphaFold models?
Karelina, M., Noh, J. J., & Dror, R. O. (2023) · doi:10.7554/elife.89386.1 · Cited by 16
Hallucination of closed repeat proteins containing central pockets
An, L., Hicks, D. R., Zorine, D., Dauparas, J., Wicky, B. I. M., Milles, L. F., Courbet, A., Bera, A. K., Nguyen, H., Kang, A., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01112-6 · Cited by 15
Structural mechanisms for VMAT2 inhibition by tetrabenazine
Dalton, M. P., Cheng, M. H., Bahar, I., & Coleman, J. A. (2024) · eLife · doi:10.7554/elife.91973.4 · Cited by 14
Savitri, D., Wahyuni, S., Bukhari, A., Djawad, K., Hatta, M., Riyanto, P., Bahar, B., Wahab, S., Hamid, F., & Rifai, Y. (2023) · Journal of Taibah University Medical Sciences · doi:10.1016/j.jtumed.2023.07.008 · Cited by 13
ILP-assisted de novo drug design
Kaalia, R., Srinivasan, A., Kumar, A., & Ghosh, I. (2016) · Machine Learning · doi:10.1007/s10994-016-5556-x · Cited by 13
Liu, Y., Moretti, R., Wang, Y., Dong, H., Yan, B., Bodenheimer, B., Derr, T., & Meiler, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00822 · Cited by 13
Shao, C., Xu, H., Sun, X., Pan, Y., Liang, X., Huang, J., He, Y., Guo, W., Ye, L., & Zhang, J. (2024) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2023.117486 · Cited by 12
Assessing the binding properties of CASP14 targets and models
Egbert, M., Ghani, U., Ashizawa, R., Kotelnikov, S., Nguyen, T., Desta, I., Hashemi, N., Padhorny, D., Kozakov, D., & Vajda, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26209 · Cited by 10
Munawaroh, H. S. H., Gumilar, G. G., Pratiwi, R. N., Khoiriah, S. F., Ningrum, A., Martha, L., Chew, K. W., & Show, P.-L. (2024) · Algal Research · doi:10.1016/j.algal.2024.103468 · Cited by 9
Mapping Protein Conformational Landscapes from Crystallographic Drug Fragment Screens
Saeed, A. A., Klureza, M. A., & Hekstra, D. R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01380 · Cited by 9
Nigl, A., Delsoglio, V., Sovic, L., Grgić, M., Malihan‐Yap, L., Myrtollari, K., Spasic, J., Winkler, M., Oberdorfer, G., Taden, A., et al. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202503464 · Cited by 9
Dey, D., Nishijima, M., Tanaka, R., Kurisu, G., Tanaka, H., & Ito, H. (2022) · Protein Science · doi:10.1002/pro.4430 · Cited by 7
Tracing Potential Covalent Inhibitors of an E3 Ubiquitin Ligase through Target-Focused Modelling
Bjij, I., Ramharack, P., Khan, S., Cherqaoui, D., & Soliman, M. E. S. (2019) · Molecules · doi:10.3390/molecules24173125 · Cited by 6
drMD: Molecular Dynamics for Experimentalists
Shrimpton-Phoenix, E., Notari, E., Kluonis, T., & Wood, C. W. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168918 · Cited by 6
Structural mechanisms for VMAT2 inhibition by tetrabenazine
Dalton, M. P., Cheng, M. H., Bahar, I., & Coleman, J. A. (2023) · doi:10.1101/2023.09.05.556211 · Cited by 6
Metadata
- Authors
- O'Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R.
- Venue
- Journal of Cheminformatics
- Published
- 2011-10-07
- Volume
- 3
- Issue
- 1
- Pages
- 33-33
- ISSN
- 1758-2946 (ISSN-L)
- OpenAlex
- W2169678694
- Cited by
- 10,939
- References
- 0
- FWCI
- 141.13
- Type
- journal article
- Language
- en
- Open access
- gold
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.