The DOI matches its Crossref record; 172 of the 172 references it cites were found in a registry or an index.
Small molecules, big targets: drug discovery faces the protein–protein interaction challenge
Open accessAbstract
Protein-protein interactions (PPIs) are of pivotal importance in the regulation of biological systems and are consequently implicated in the development of disease states. Recent work has begun to show that, with the right tools, certain classes of PPI can yield to the efforts of medicinal chemists to develop inhibitors, and the first PPI inhibitors have reached clinical development. In this Review, we describe the research leading to these breakthroughs and highlight the existence of groups of structurally related PPIs within the PPI target class. For each of these groups, we use examples of successful discovery efforts to illustrate the research strategies that have proved most useful.
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?
In the citing paper
“The inhibition of PPIs has arguably received more attention in the past and has proved to be challenging but still feasible.2,3”
From Chasing molecular glue degraders: screening approaches (2022) · cites this paper as [2]
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
In Vivo Activation of the p53 Pathway by Small-Molecule Antagonists of MDM2
Vassilev, L. T., Vu, B. T., Graves, B., Carvajal, D., Podlaski, F., Filipovic, Z., Kong, N., Kammlott, U., Lukacs, C., Klein, C., et al. (2004) · Science · doi:10.1126/science.1092472 · Cited by 4,661
Selective inhibition of BET bromodomains
Filippakopoulos, P., Qi, J., Picaud, S., Shen, Y., Smith, W. B., Fedorov, O., Morse, E. M., Keates, T., Hickman, T. T., Felletar, I., et al. (2010) · Nature · doi:10.1038/nature09504 · Cited by 4,262
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 4,196
Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A. C., Mínguez, P., Doerks, T., Stark, M., Muller, J., Bork, P., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq973 · Cited by 3,469
ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets
Souers, A. J., Leverson, J. D., Boghaert, E. R., Ackler, S. L., Catron, N. D., Chen, J., Dayton, B. D., Ding, H., Enschede, S. H., Fairbrother, W. J., et al. (2013) · Nature Medicine · doi:10.1038/nm.3048 · Cited by 3,058
Principles of protein-protein interactions
Jones, S., & Thornton, J. M. (1996) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.93.1.13 · Cited by 2,684
The Future of Peptide‐based Drugs
Craik, D. J., Fairlie, D. P., Liras, S. I., & Price, D. A. (2012) · Chemical Biology & Drug Design · doi:10.1111/cbdd.12055 · Cited by 2,034
Reaching for high-hanging fruit in drug discovery at protein–protein interfaces
Wells, J. A., & McClendon, C. L. (2007) · Nature · doi:10.1038/nature06526 · Cited by 2,002
ABT-263: A Potent and Orally Bioavailable Bcl-2 Family Inhibitor
Tse, C., Shoemaker, A. R., Adickes, J., Anderson, M. G., Chen, J., Jin, S., Johnson, E. F., Marsh, K. C., Mitten, M. J., Nimmer, P., et al. (2008) · Cancer Research · doi:10.1158/0008-5472.can-07-5836 · Cited by 1,992
Anatomy of hot spots in protein interfaces
Bogan, A. A., & Thorn, K. S. (1998) · Journal of Molecular Biology · doi:10.1006/jmbi.1998.1843 · Cited by 1,988
Flagged first, then by citations
Cited by
Sorted by citations
Items 801–900 of 1,001
How do you mend inactive tumor suppressor mutants? You glue them!
Hata, A., & Lagna, G. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2021.04.023 · Cited by 3
Development of cyclic peptides that can be administered orally to inhibit disease targets
Merz, M. L., & Heinis, C. (2023) · Nature Chemical Biology · doi:10.1038/s41589-023-01505-0 · Cited by 3
Predicting the Dynamic Interaction of Intrinsically Disordered Proteins
Zheng, Y., Li, Q., Freiberger, M. I., Song, H., Hu, G., Zhang, M., Gu, R., & Li, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00930 · Cited by 3
Applications of Molecular Dynamics Simulation and MM-PBSA Methods in Discovery of Veterinary Drugs
Kumar, N., Mandal, P., Kumar, B., Rani, P., & Singh, D. V. (2025) · Springer eBooks · doi:10.1007/978-981-97-7395-4_14 · Cited by 3
Majmudar, P. R., & Keri, R. A. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110235 · Cited by 3
He, C., Zhang, J., Zhang, R., Liu, L., Luo, J., Zhang, W., & Fang, B. (2025) · Drug Design Development and Therapy · doi:10.2147/dddt.s518489 · Cited by 3
Nowacki, M., Menezes, F., Pykacz, E., Popiołek, M., Napolitano, V., Krishna, C. K., Kalel, V. C., Erdmann, R., Fröhlich, T., Plettenburg, O., et al. (2025) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2025.117979 · Cited by 4
Revisiting Protein–Protein Docking: A Systematic Evaluation Framework
Jiang, L., Zhang, K., Zhu, K., Wang, Y., Kang, Y., & Hou, T. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01399 · Cited by 3
Computer-Aided Drug Design for the Identification of Novel Antischistosomal Compounds
Viana, J. D. O., Scotti, L., & Scotti, L. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0635-3_2 · Cited by 3
Srdanović, S., Hegedüs, Z., Warriner, S. L., & Wilson, A. J. (2022) · RSC Chemical Biology · doi:10.1039/d2cb00025c · Cited by 3
Fragment hopping protocol for the design of small-molecule protein–protein interaction inhibitors
Kell, S. R., Wang, Z., & Ji, H. (2022) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2022.116879 · Cited by 3
Mukherjee, A., Cuanalo-Contreras, K., Sood, A., & Soto, C. A. (2022) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2022.101311 · Cited by 3
FUNDAMENTALS OF PROTEIN-PROTEIN INTERACTIONS AND THEIR METHODS OF CHARACTERIZATION
Audah, K. A. (2021) · Bioinformatics and Biomedical Research Journal · doi:10.11594/bbrj.04.02.04 · Cited by 3
Unveiling the biological interface of protein complexes by mass spectrometry‐coupled methods
Shin, G., & Lim, S. I. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26459 · Cited by 3
Lee, J., Seok, C., Ham, S., & Chong, S. (2022) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26458 · Cited by 2
Nagamani, V., & Sriraghavan, K. (2024) · European Journal of Organic Chemistry · doi:10.1002/ejoc.202400575 · Cited by 3
Turvey, S., Muench, S. P., Issad, T., Fishwick, C. W. G., Kearney, M. T., & Simmons, K. J. (2024) · Growth Hormone & IGF Research · doi:10.1016/j.ghir.2024.101607 · Cited by 3
Chinigò, G., Ruffinatti, F. A., & Munaron, L. (2024) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2024.189226 · Cited by 3
Wu, S., Xiao, M., Liu, F., Hong, H.-Y., Ding, C., Zhang, X., & Xie, W. (2024) · Discover Oncology · doi:10.1007/s12672-024-01661-2 · Cited by 4
Prediction of protein interactions with function in protein (de-)phosphorylation
Vagiona, A.-C., Notopoulou, S., Zdráhal, Z., Gonçalves-Kulik, M., Petrakis, S., & Andrade-Navarro, M. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319084 · Cited by 4
Jin, G. (2025) · Nature Chemical Biology · doi:10.1038/s41589-025-01903-6 · Cited by 3
In silico ligand‐based modeling of hBACE ‐1 inhibitors
Subramanian, G., & Poda, G. I. (2017) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13147 · Cited by 3
Li, Q., Constantino, L. F. D. S., & Spies, M. (2018) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2017.11.036 · Cited by 3
Hirose, H., Hideshima, T., Katoh, T., & Suga, H. (2019) · ChemBioChem · doi:10.1002/cbic.201900039 · Cited by 3
Fernández, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00651 · Cited by 3
Manasoe, B., Mmbengwa, V. M., & Lekunze, J. N. (2022) · Journal of Agribusiness and Rural Development · doi:10.17306/j.jard.2022.01533 · Cited by 3
Prediction and analysis of hot region in protein-protein interactions
Lin, X., & Zhang, X. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822758 · Cited by 3
Using Fragment Based Drug Discovery to Target Epigenetic Regulators in Cancer
Young, D. W. (2017) · MOJ Bioequivalence & Bioavailability · doi:10.15406/mojbb.2017.04.00062 · Cited by 3
High-throughput screening for intrinsically disordered proteins by using biophysical methods
Abián, O., Vega, S., Neira, J. L., & Velázquez‐Campoy, A. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819132-3.00017-8 · Cited by 3
Zhu, M., Pan, J., Xu, M., Cao, J., Zhu, Y., Zhu, H., Su, G., & Yu, Y. (2022) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2022.131710 · Cited by 3
Geny, S., Pichard, S., Brion, A., Renaud, J.-B., Jacquemin, S., Concordet, J., & Poterszman, A. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-1126-5_3 · Cited by 3
Protein–Protein Interactions and Genetic Disease
Brender, J., & Zhang, Y. (2017) · Encyclopedia of Life Sciences · doi:10.1002/9780470015902.a0026856 · Cited by 3
Predicting PROTAC-targeted Degradation and Designing Androgen Receptor Degraders with AiPROTACPreprint
Zhang, L., Sun, R., Li, X., Ren, C., Long, Y., Tan, W., Wang, X., Zou, K., Yang, X., Wu, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.26.640266 · Cited by 5
Sorbara, M., & Béry, N. (2021) · Biochemical Society Transactions · doi:10.1042/bst20201059 · Cited by 2
α-Hydrazino Acid Insertion Governs Peptide Organization in Solution by Local Structure Ordering
Kavčič, L., Ilc, G., Wang, B., Vlahoviček‐Kahlina, K., Jerić, I., & Plavec, J. (2024) · ACS Omega · doi:10.1021/acsomega.4c00804 · Cited by 2
Liu, S., Wang, R., Wang, X., Yu, L., & Wang, C. (2024) · Colloid & Polymer Science · doi:10.1007/s00396-024-05300-8 · Cited by 2
Garrido-Palazuelos, L. I., Aguirre-Sánchez, J. R., Sandoval-González, M. F., Mukhtar, M., Guerra-Meza, O., & Khan, H. A. (2024) · Molecular Biotechnology · doi:10.1007/s12033-024-01329-w · Cited by 2
Cairang, N., Wu, Y., Zhi, S., Tang, J., Tie, X., Zhan, D., Lu, G., Shi, Y., & Zhao, Q. (2025) · RSC Advances · doi:10.1039/d4ra06512c · Cited by 2
Nagamani, V., & Sriraghavan, K. (2025) · Synthesis · doi:10.1055/a-2661-4104 · Cited by 2
Alphappimi: a comprehensive deep learning framework for predicting PPI-modulator interactions
Liu, D., Song, T., Wang, S., Li, X., Han, P., Wang, J., & Wang, S. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01077-2 · Cited by 2
Molecular basis of the activity of a photoswitchable histone deacetylase inhibitor
Queizán, M., Teijeira, M., & Hermida‐Ramón, J. M. (2025) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2025.128488 · Cited by 2
Ebeid, I. A., Tang, H., & Gu, P. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1651623 · Cited by 2
Ancient and Emerging Nanostructures for Innovations to Fight Head and Neck Cancer
Kummer, N., Acet, Ö., Acet, B. Ö., Blueggel, M., Khamis, A., Gül, D., Knauer, S. K., & Stauber, R. H. (2026) · Cells · doi:10.3390/cells15040339 · Cited by 3
Agarwala, P., Sadhukhan, S., Mondal, S., & Sasmal, D. K. (2026) · ChemBioChem · doi:10.1002/cbic.202500772 · Cited by 2
Li, T., Huang, H., Zeng, Q., Zeng, L., Yang, Z., Shen, J., Li, S., Yang, H., Liu, Y., & Wu, C. (2026) · BMEMat · doi:10.1002/bmm2.70095 · Cited by 2
SE(3)-PROTACs: Geometric deep learning for PROTAC degradation prediction
Kothakapu, A. R., Madugula, S., Gandeed, S. B. S., Yadlapati, K. S. S., Sury, S., & Kondaparthi, V. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag228 · Cited by 2
HighPlay2: Structure-guided design of cyclic peptide candidates containing non-canonical amino acids
Lin, H., Wang, W. M., Ning, Z. H., Shang, T., Fan, Y., Zhang, C., Guo, J., Wang, X., & Duan, H. (2026) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2026.119160 · Cited by 2
Small Molecule Inhibitors Targeting New Targets of Protein-Protein Interactions
Yue, L., Wan, W., Xu, P., Li, L., Wang, C., Zhang, Y., Xu, H., Zhang, R., Hu, J., Lu, W., et al. (2018) · Springer eBooks · doi:10.1007/978-981-13-0773-7_7 · Cited by 2
Liu, L.-L., Sun, J., Xu, Y., He, L., Chandramouli, K., & Qian, P. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00228 · Cited by 2
de Souza, A., Castro‐Borges, W., de Andrade, M. H. G., Maia, Y. C. D. P., Goulart, L. R., Lanna, E. G., de Brito, A. C. F., Barboza, A. P. M., Mosqueira, V. C. F., & Rubio, K. T. S. (2020) · Drug Delivery and Translational Research · doi:10.1007/s13346-020-00826-0 · Cited by 2
Wang, Y., Yin, F., & Li, Z. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2489-0_12 · Cited by 2
The potential of RANKL reverse signaling as a novel pharmacological target
Honma, M. (2023) · Folia Pharmacologica Japonica · doi:10.1254/fpj.22145 · Cited by 2
Becker, L., & Plückthun, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-34647-1 · Cited by 2
Molecular simulation studies on a zwitterionic peptide-dendrimer conjugate for integrin αvβ3 binding
Lin, X., Xu, N., Li, C., Wu, Z., Chen, S., Shi, Y., & He, Y. (2023) · Biointerphases · doi:10.1116/6.0002713 · Cited by 2
Drug Design Methods to Regulate Protein–Protein Interactions
Poluri, K. M., Gulati, K., Tripathi, D. K., & Nagar, N. (2023) · Springer eBooks · doi:10.1007/978-981-99-2423-3_6 · Cited by 2
A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models
Chen, X., Liu, J., Park, N., & Cheng, J. (2024) · Biomolecules · doi:10.3390/biom14050574 · Cited by 2
Denktaş, M., & Fişekçioğlu, İ. B. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub452.c1931 · Cited by 2
Alt Maksimum ve Maksimum Egzersiz Testlerinde Oksijen ve Laktat Kinetiği
Shahidi, S. H. (2024) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub452.c1930 · Cited by 2
Makhlouf, J., Timothy, R. A., Valkonen, A., Abuelizz, H. A., Al‐Salahi, R. A., Bakri, Y. E., & Sta, W. S. (2024) · Journal of Inorganic and Organometallic Polymers and Materials · doi:10.1007/s10904-024-03345-4 · Cited by 2
Tang, J., Tie, X., Zhi, S., Wang, Z., Zhao, Q., Qu, Z., Lu, G., Li, Q., Wu, Y., & Shi, Y. (2024) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2024.107741 · Cited by 2
Zhou, K. X. T., & Bujold, K. E. (2024) · ACS Chemical Biology · doi:10.1021/acschembio.4c00311 · Cited by 2
Zhang, W., Li, S., Wang, Y., Liu, S., Liu, L., Deng, Z., Mo, S., Chen, M., Li, Z., Wang, R., et al. (2025) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c18411 · Cited by 2
Liu, P., Zhang, S., Zhang, T., Si, Y., Liu, Z., Qian, X., Wu, Y., Liang, Y., Sun, W. C., Akkaya, E. U., et al. (2025) · Chinese Chemical Letters · doi:10.1016/j.cclet.2025.110966 · Cited by 2
Precision peptide disruptors: The next generation of targeted therapeutics in oncology
O'Gorman, B., Cooke, S. F., & Blair, C. M. (2025) · Cellular Signalling · doi:10.1016/j.cellsig.2025.111783 · Cited by 2
Dhanya, T. M., Varughese, J. K., Rajimon, K. J., Krishna, G. A., Kurup, M. R. P., Philip, S., Augustine, M., Divya, K. M., & Mohanan, P. V. (2025) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2025.142315 · Cited by 2
Bromodomain Inhibition and Its Application to Human Disease
Dupper, N. J., Zhou, Y., Govin, J., & McKenna, C. E. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-813939-4.00011-5 · Cited by 2
Protein-Protein Interactions in Malaria: Emerging Arena for Future Chemotherapeutics
Pasupureddy, R., Seshadri, S., Dixit, R., & Pandey, K. C. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.89217 · Cited by 2
Design and applications of catalytic metallodrugs containing the ATCUN motif
Yu, Z., & Cowan, J. A. (2019) · Advances in inorganic chemistry · doi:10.1016/bs.adioch.2019.10.005 · Cited by 2
Thrombopoietin receptor‐based protein–protein interaction screening (THROPPIS)
Horikawa, M., Kakiuchi, Y., Kashima, D., Ogawa, K., & Kawahara, M. (2021) · Biotechnology and Bioengineering · doi:10.1002/bit.27975 · Cited by 2
Awuni, E. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.814935 · Cited by 2
Peptide‐Based Inhibitors that Target the Docking Site of ERK2
Solomon, O., Shpilt, Z., Sapir, H., Marom, S., Bibas, S., Chen, Y., Tshuva, E. Y., Yitzchaik, S., & Friedler, A. (2022) · Israel Journal of Chemistry · doi:10.1002/ijch.202200041 · Cited by 2
Usman, M. A., Salman, A. A., Ibrahim, M. A., Furukawa, K., & Yamasaki, K. (2023) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14225 · Cited by 2
Protein–Protein Interaction for Drug Discovery
Brambila, B., Martelli, A. C. F. S., Barcelos, M. P., Antão, S. C., da Silva, C. H. T. D. P., & Novo‐Mansur, M. T. M. (2023) · Engineering materials · doi:10.1007/978-3-031-46545-1_12 · Cited by 2
Mirón, N. S., Tapia, L., Solà, J., Pérez, Y., & Alfonso, I. (2023) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.3c00455 · Cited by 2
Finding a Silver Bullet in a Stack of Proteins
Makhlynets, O. V., & Korendovych, I. V. (2017) · Biochemistry · doi:10.1021/acs.biochem.7b01177 · Cited by 2
Katagiri, N., Nagatoishi, S., Tsumoto, K., & Endo, H. (2019) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2019.07.006 · Cited by 2
Mercadal, P. A., Motrich, R. D., & Coronado, E. A. (2019) · Scientific Reports · doi:10.1038/s41598-019-49583-2 · Cited by 2
Ferreira, L. L. G., & Andricopulo, A. D. (2021) · Wiley eBooks · doi:10.1002/0471266949.bmc141.pub2 · Cited by 2
A genetically encoded photo-proximity labeling approach for mapping protein territoriesPreprint
Hananya, N., Ye, X., Koren, S., & Muir, T. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.30.502153 · Cited by 2
Target-specific design of drug-like PPI inhibitors via hot-spot-guided generative deep learningPreprint
Sun, H., Li, J., Zhang, Y., Lin, S., Chen, J., Tan, H., Wang, R., Mao, X., Zhao, J., Li, R., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.29.620869 · Cited by 2
Affinity-based DEL Selections with Different Target Types: Overview and Achievements
Chen, Q., & Israel, D. I. (2025) · Royal Society of Chemistry eBooks · doi:10.1039/9781788016032-00001 · Cited by 2
Nada, H., Calvo‐Barreiro, L., Cho, S., Upadhyay, S., Meanwell, N. A., & Gabr, M. T. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.21.666047 · Cited by 2
Foldamers as Inhibitors of Aberrant Protein–Protein Interactions
Stillman, N. H., Dohoney, R. A., Baysah, C. Z., & Kumar, S. (2025) · Wiley eBooks · doi:10.1002/9783527845828.ch8 · Cited by 2
Stapled peptidomimetic therapeutics
Kannan, S., Lama, D., Tan, Y. S., Li, J., & Verma, C. S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-820141-1.00022-4 · Cited by 1
Carbene Footprinting Directs Design of Genetically Encoded Proximity-Reactive Protein Binders
Ye, H., Zhu, Y., Kong, Y., Wen, H., Lu, W., Wang, D., Tang, S., Zhan, M., Lu, G., Shao, C., et al. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c00424 · Cited by 1
Naylon, S. H., Richaud, A. D., Zhao, G., Bui, L., Dufresne, C. P., Wu, C., Wangpaichitr, M., Savaraj, N., & Roche, S. P. (2024) · RSC Chemical Biology · doi:10.1039/d4cb00174e · Cited by 1
Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER
Zhang, J., Zhou, F., Liang, X., & Kurgan, L. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4196-5_15 · Cited by 1
Giménez, D., Walko, M., Miles, J. A., Bayliss, R. W., Wright, M. H., & Wilson, A. J. (2024) · Chemical Science · doi:10.1039/d4sc06100d · Cited by 1
The Role of Artificial Intelligence in the Discovery of New Drugs
Barcelos, M. P., & da Silva, C. H. T. D. P. (2024) · Engineering materials · doi:10.1007/978-3-031-75984-0_14 · Cited by 1
Structural analysis of EPOP BC-box binding to the elongin BC complex
Kim, S., Yeo, H., & Lee, B. I. (2025) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2025.151691 · Cited by 1
Sakalauskaite, G., Weingärtner, M., Ebert, S., Boot, G. F., Bock, T., Birk, J., Tsachaki, M., Gallon, J. W., Piscuoglio, S., & Odermatt, A. (2025) · Cell & Bioscience · doi:10.1186/s13578-025-01388-9 · Cited by 1
Jan, R., Hussain, A. B., Assad, A., & Bhat, B. A. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10498-y · Cited by 1
Shrestha, P., Singh, S. S., Dahal, A., Prasasty, V. D., Chowdhury, A., Majumdar, D., Gu, X., Johnson, W., Zhang, D., Billadeau, D. D., et al. (2025) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.5c00336 · Cited by 1
Topologically Tunable α‐Helix Mimetics for Targeting Protein–Protein Interactions
Lee, T. K., Chen, L., Chen, C., Neal, H., & Ahn, J. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202508209 · Cited by 1
Topologically Tunable α‐Helix Mimetics for Targeting Protein–Protein Interactions
Lee, T. K., Chen, L., Chen, C., Neal, H., & Ahn, J. (2025) · Angewandte Chemie · doi:10.1002/ange.202508209 · Cited by 1
Formation of complex cyclic compounds via a dehydrogenative Diels–Alder reaction
Xu, Z. Y., Yu, L., Chang, M., Qiang, R., Hu, Q., & Hu, Y. (2025) · Organic & Biomolecular Chemistry · doi:10.1039/d5ob01117e · Cited by 1
Deep learning for affinity prediction and interface prediction in molecular interactions
Bian, Y., Wu, H., & Yan, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-332908-1.00024-6 · Cited by 1
Nasibov, T. F., Gorokhova, A. V., Бразовский, К. С., Ryzhkova, A., Porokhova, E. D., Avdeeva, E. Y., Белоусов, М. В., Kokorev, O. V., & Хлусов, И. А. (2025) · Pharmaceutical Research · doi:10.1007/s11095-025-03934-z · Cited by 1
Khan, M. W. A., Alam, M. J., Sherwani, S., Alouffi, S., Al-Motair, K. A., Khan, S. A., & Khan, S. N. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262010038 · Cited by 1
LPS-focused nanomedicine for potent antibacterial therapy
Choudhury, S., Mishra, R. R., Maji, S., Arora, K., Singh, M. P., Mishra, J., & Panda, J. J. (2025) · Nanomedicine · doi:10.1080/17435889.2025.2574839 · Cited by 1
Metadata
- Authors
- Scott, D. E., Bayly, A. R., Abell, C., & Skidmore, J.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Abell, Chris
- Skidmore, John
- Venue
- Nature Reviews Drug Discovery
- Published
- 2016-04-11
- Volume
- 15
- Issue
- 8
- Pages
- 533-550
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2329592726
- Cited by
- 1,137
- References
- 172
- Citations by year
- 2026: 101 · 2025: 121 · 2024: 120 · 2023: 115 · 2022: 117 · 2021: 128 · 2020: 120 · 2019: 106 · 2018: 107 · 2017: 83 · 2016: 16
- FWCI
- 8.11
- Type
- journal article
- Language
- en
- Open access
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- protein-protein interaction, PPI inhibitors, small molecule drug discovery, drug target identification, structure-based drug design
- MeSH terms
- Animals, Humans, Models, Molecular, Proteins, Protein Structure, Tertiary, Computational Biology, Protein Interaction Domains and Motifs, Small Molecule Libraries, Drug Discovery
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