Cited by 1,000Computational Drug Discovery MethodsChemical Synthesis and AnalysisProtein Degradation and Inhibitors

The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.

Twenty years on: the impact of fragments on drug discovery

Erlanson, Daniel A., Fesik, Stephen W., Hubbard, Roderick E., Jahnke, Wolfgang, Jhoti, Harren·2016·Nature Reviews Drug Discovery

DOI 10.1038/nrd.2016.109

Open this paper in CoChat — free account

Abstract

After 20 years of sometimes quiet growth, fragment-based drug discovery (FBDD) has become mainstream. More than 30 drug candidates derived from fragments have entered the clinic, with two approved and several more in advanced trials. FBDD has been widely applied in both academia and industry, as evidenced by the large number of papers from universities, non-profit research institutions, biotechnology companies and pharmaceutical companies. Moreover, FBDD draws on a diverse range of disciplines, from biochemistry and biophysics to computational and medicinal chemistry. As the promise of FBDD strategies becomes increasingly realized, now is an opportune time to draw lessons and point the way to the future. This Review briefly discusses how to design fragment libraries, how to select screening techniques and how to make the most of information gleaned from them. It also shows how concepts from FBDD have permeated and enhanced drug discovery efforts.

Integrity signals

Registry

Crossref · 10.1038/nrd.2016.109

Cites

110 of 110 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays
Baell, J. B., & Holloway, G. A. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm901137j · Cited by 3,963
An inhibitor of Bcl-2 family proteins induces regression of solid tumours
Oltersdorf, T., Elmore, S. W., Shoemaker, A. R., Armstrong, R. C., Augeri, D. J., Belli, B., Bruncko, M., Deckwerth, T. L., Dinges, J., Hajduk, P. J., et al. (2005) · Nature · doi:10.1038/nature03579 · Cited by 3,407
Self-citation
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
K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions
Ostrem, J. M., Peters, U., Sos, M. L., Wells, J. A., & Shokat, K. M. (2013) · Nature · doi:10.1038/nature12796 · Cited by 2,557
Discovering High-Affinity Ligands for Proteins: SAR by NMR
Shuker, S. B., Hajduk, P. J., Meadows, R. P., & Fesik, S. W. (1996) · Science · doi:10.1126/science.274.5292.1531 · Cited by 2,045
Self-citation
Crystal structure of the human β2 adrenergic G-protein-coupled receptor
Rasmussen, S. G. F., Choi, H., Rosenbaum, D. M., Kobilka, T. S., Thian, F. S., Edwards, P. C., Burghammer, M. C., Ratnala, V. R. P., Sanishvili, R., Fischetti, R. F., et al. (2007) · Nature · doi:10.1038/nature06325 · Cited by 1,935
Ligand efficiency: a useful metric for lead selection
Hopkins, A. L., Groom, C. R., & Alex, A. (2004) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03069-7 · Cited by 1,895
Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300415d · Cited by 1,505
A ‘Rule of Three’ for fragment-based lead discovery?
Congreve, M., Carr, R. A. E., Murray, C. W., & Jhoti, H. (2003) · Drug Discovery Today · doi:10.1016/s1359-6446(03)02831-9 · Cited by 1,415
Self-citation
Structure of a β1-adrenergic G-protein-coupled receptor
Warne, T., Serrano‐Vega, M. J., Baker, J. G., Moukhametzianov, R., Edwards, P. C., Henderson, R. A., Leslie, A. G. W., Tate, C. G., & Schertler, G. F. X. (2008) · Nature · doi:10.1038/nature07101 · Cited by 1,377

Flagged first, then by citations

Show all 110 references

Research works printed but not linked: 1

Woolford, A. J. Experiences with fragment libraries at Astex. Presented at the Fragment-based Lead Discovery (FBLD) Conference. (2014).

Sorted by citations

Items 901–1,000 of 1,000

Computational Approaches in Evaluating the 5-HT Subtype Receptor Mechanism of Action for Developing Novel Chemical Entities
Chauhan, A., & Avti, P. (2024) · Frontiers in computational chemistry · doi:10.2174/9789815179033124070006
Fragment‐based Drug Design and Drug Discovery
Oliveira, A. M., & Rudrapal, M. (2024) · doi:10.1002/9781394249190.ch10
The SOSA Approach to Drug Discovery
Handler, N., Pozník, M., & Buschmann, H. (2024) · Wiley eBooks · doi:10.1002/9781394182862.ch21
Screening Technologies for Lead Structure Discovery
Klebe, G. (2024) · Springer eBooks · doi:10.1007/978-3-662-68998-1_7
Höherer Kontrast in 1 H‐Observierten NMR Ligandenscreenings mit dem PEARLScreen‐Experiment
Lorz, N., Czarniecki, B., Loss, S., Meier, B., & Gossert, A. D. (2025) · Angewandte Chemie · doi:10.1002/ange.202423879
Combining MicroED and native mass spectrometry for structural discovery of enzyme-biosynthetic inhibitor complexesPreprint
Vlahakis, N. W., Flowers, C. W., Liu, M., Agdanowski, M. P., Johnson, S. E., Summers, J. A., Keyser, C., Russell, P., Rose, S. L., Orlans, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.20.638743
ControlMol: Adding Substructure Control To Molecule Diffusion Models
Qi, Z., Liu, Z., Zhang, J., Cao, H., Xu, X., & Li, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888521
“To reduce the size of the searching space, the strategy fragment-based drug design(FBDD) [13] generates molecules from fragments [14].”
RNA modulation
Garner, A. L., Bonnet, M., Carozza, C. A., Eby, O. G., & Dean, M. E. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00015-7
Advancing Target Validation and Ligandability Assessment: Fragment Screening via 19F NMR Spectroscopy
Salvi, N., Dalvit, C., Frances, O., Vallée, F., & Rak, A. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4418-8_13
Rational Method for Structural Simplification as Key Step in Hit Discovery: The Case of FGFR2 and IGF1R Dual Inhibitors
Torres-Urtizberea, E., Borrell, J. I., de la Bellacasa, R. P., & Estrada‐Tejedor, R. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26094457
“Such strategies are often based on high-throughput screening (HTS) [1], structure-based drug design (SBDD) [2], fragment-based lead generation (FBLG) [3] or the most used, knowledge-based lead generation (KBLG) [4].”
Fragment‐Based Drug Discovery of Novel High‐affinity, Selective, and Anti‐inflammatory Inhibitors of the Keap1‐Nrf2 Protein‐Protein Interaction
Lin, C., Narayanan, D., Barreca, M., Poulsen, C., da Costa, L. S., Chen, X., Wichman, K. G., Charley, C. A., Lindsay, J. L., Dezfouli, M., et al. (2025) · Angewandte Chemie · doi:10.1002/ange.202508121
Enantiocomplementary Bioreduction of Flexible Ring N‐(3‐Oxobutyl)Heterocycles Providing Enantiopure Chiral Fragments for Drug Discovery
Honvári, M. G., Mócza, L. A., Kucsinka, B. A., Csuka, P., Bódai, V., Scrob, D. M., Poppe, L., & Hornyánszky, G. (2025) · ChemBioChem · doi:10.1002/cbic.202500601
Discovery of RNA-binding fragments using biolayer interferometry
Panchal, V. N., Husmann, J.-Å., Günther, K., Zeeshan, M., Haug, B. E., & Brenk, R. (2025) · RSC Medicinal Chemistry · doi:10.1039/d5md00673b
“13 However, FBDD approaches are underexplored for RNA targets.”
High‐Throughput Synthesis and Screening of a Cyanimide Library Identifies Selective Inhibitors of ISG15‐Specific Protease mUSP18
Kooij, R., Pol, V., Gan, J., van Doodewaerd, B. R., Sapmaz, A., Alonso, M. C., Fritz, G., Knobeloch, K., & Geurink, P. P. (2025) · Angewandte Chemie · doi:10.1002/ange.202510941
Generative chemistry
Boe, D., Elbrecht, D., Lee, A. A., Robinson, M. C., & Schroeder, T. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00059-5
Regio‐ and Stereocontrolled‐Synthesis of a Heterocycle Fragment Collection Using Palladium Catalyzed C‐H Arylation
Piticari, A., Antermite, D., Linkhorn, H. J., Larionova, N. A., Webster, M. P., & Bull, J. A. (2025) · Chemistry - A European Journal · doi:10.1002/chem.202503126 · Cited by 1
A Fragment-based Competitive 19F LB-NMR Platform For Hotspot-directed Ligand Profiling
McCarthy, W. J., Thomas, S. E., Olaleye, T. O., Boland, J. A., Floto, R. A., Williams, G., Blundell, T. L., Coyne, A. G., & Abell, C. (2024) · Apollo (University of Cambridge) · doi:10.17863/cam.109535
Cell wall target fragment discovery using a low‐cost, minimal fragment library
Yan, K., Stanley, M., Raimi, O. G., Ferenbach, A. T., Dorfmueller, H. C., & van Aalten, D. M. F. (2026) · FEBS Letters · doi:10.1002/1873-3468.70281
Ubiquitination‐Driven Reprogramming of Proteostasis in Metastasis
Wei, D., Chen, J., & Xu, Y. (2026) · Advanced Science · doi:10.1002/advs.202522165 · Cited by 1
“Initial fragments are then optimized through structure-guided linking or growing to simultaneously engage multiple hotspots, improving affinity toward lead-like properties [ 43, 44 ].”
Integrating Functional Response and Target Binding for Mechanism-Centered Drug Screening by High-Mass MALDI-MS
Tan, C., Gao, Y., Buggert, M., Zhou, Y., & Zenobi, R. (2026) · ACS Central Science · doi:10.1021/acscentsci.5c01944
Small molecule and fragment-based phenotypic screening for novel building blocks with antimycobacterial activity
Konaklieva, M. I., Arora, K., Boshoff, H. I., & Plotkin, B. J. (2026) · Exploration of Drug Science · doi:10.37349/eds.2026.1008145
Structure-guided discovery strategies for Trop2-targeted small molecule inhibitors
Moon, D. O. (2026) · Bulletin du Cancer · doi:10.1016/j.bulcan.2025.11.016
Frags2Drugs: A Novel In Silico Fragment-Based Approach to the Discovery of Kinase Inhibitors
Peyrat, G., Bournez, C., Krezel, P., Gally, J., Bourg, S., Aci‐Sèche, S., & Bonnet, P. (2026) · Pharmaceuticals · doi:10.3390/ph19020308
“The only common substructure they share is the pyrrolo[2,3-b]pyridine seed.”
Evaluation of Ginsenosides and Their Derivatives From Panax ginseng as Aromatase Inhibitors for Breast Cancer Treatment—An in silico study
Madhan, J. G., Sekaran, S., Akshaya, R. N., Venkatesan, K., Kirupanandasamy, K., Vijayakumar, K., Jenifer, N., & Paul, B. C. (2026) · BioMed Research International · doi:10.1155/bmri/5538317
“The methodological approach taken in this research focuses on the robustness of our ﬁ ndings while, at the same time, overcoming the shortcomings of earlier studies that did not have such stringent validation [22].”
Single-scan detection of ligand-binding using hyperpolarization and low-field relaxation
Narwal, P., Lorz, N., Minaei, M., Gossert, A. D., & Meier, B. (2026) · Communications Chemistry · doi:10.1038/s42004-026-01934-7
Generative artificial intelligence for de novo drug design
Ali, A. M., Qazi, S., Almuqdadi, H. T. A., Neeraj, Majeed, O. S., Majeed, A. A., Temur, A. A., Murshedi, G. H. M. A., & Waheed, M. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44415-9.00018-1
Discovery of Ultrapotent Heterodimeric Peptide Ligands Using Library-vs-Library RaPID Selections
Ohno, Y., Vinogradov, A. A., & Suga, H. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.5c23173
Développement d'outils de chémoinformatique pour l'identification d'inhibiteurs de protéines kinases à partir de fragments
Gally, J. (2017) · theses.fr (ABES) · doi:10.70675/c04eeb3az351fz48ddza731z6ad3d5044e23
Regiospecific coupling of dehydroacetic acid and hydrazines leading to bioactive heterocycles
Hornyánszky, Á., Kovács, P., Bereczki, H. F., Csorba, N., Havasi, D., Turczel, G., Szalai, T. V., Keserű, G. M., & Ábrányi‐Balogh, P. (2026) · Tetrahedron Chem · doi:10.1016/j.tchem.2026.100159
Méthyltransférases des filovirus et autres mononégavirus : caractérisation, originalités et drug design
Martin, B. (2017) · theses.fr (ABES) · doi:10.70675/bdbbffa0za5a7z46e6z9494z59bdacd89ba5
Diversité structurale des Glutathion Transférases fongiques des classes Oméga et Xi et identification de leurs ligands par des approches cristallographiques
Schwartz, M. (2018) · doi:10.70675/18481b6bz74b8z4894z8fe8z7c6742e986af
Les méthyltransférases de la coiffe du MERS-CoV : étude fonctionnelle et recherches d'inhibiteurs
Aouadi, W. (2017) · theses.fr (ABES) · doi:10.70675/8e1435e3zc63az4f17z8e10zc76c0a7a533b
Régulation post-traductionnelle et recherche d'inhibiteurs de la protéine FadD32, essentielle à la biosynthèse des acides mycoliques chez Mycobacterium tuberculosis
Le, N.-H. (2017) · theses.fr (ABES) · doi:10.70675/04b7736cz4f03z44daz8ed2zaa5abe3e8212
Méthodes RMN pour la découverte de nouveaux ligands ciblant les récepteurs couplés aux protéines G
Raingeval, C. (2019) · theses.fr (ABES) · doi:10.70675/8b962ab5z793az4112z84e4zbc9f4e566f31
Des inhibiteurs de méthyltransférases de l'ADN au développement de sondes chimiques pour l'identification de modulateurs épigénétiques dérégulés dans les cancers
Pechalrieu, D. (2017) · theses.fr (ABES) · doi:10.70675/d2b4bfd6zfd52z4582zbb64zf2988487f5cb
Conception d'un logiciel pour la recherche de nouvelles molécules bioactives
Bournez, C. (2019) · theses.fr (ABES) · doi:10.70675/1a4f06f0zdfa4z41efza87az70239c45c50c
Développement et caractérisation d'outils immunologiques dirigés contre des récepteurs membranaires d'intérêt thérapeutique
Hartmann, L. (2019) · doi:10.70675/dba0c0a8zf24fz4875z8b27z10f82633f5c6
Développement d’une nouvelle approche de criblage et de caractérisation d’interactions ligand-protéine membranaire par chromatographie de faible affinité miniaturisée
Lecas, L. (2019) · doi:10.70675/b2626169z07ebz4d3bzb054z793455cd3f0e
A yeast-based assay to screen protein-protein interaction modulators
Sudevan, A. (2021) · theses.fr (ABES) · doi:10.70675/9ab04dcaz498fz43ebz8762z012f3f86154c
Développement d'une chimie combinatoire encodée pour l'identification de ligands spécifiques d'ADN G-quadruplex
Rieber, P. (2022) · theses.fr (ABES) · doi:10.70675/bceae190z058dz4de0zb6b4z9d77e9554833
Ciblage d'ARN non codants oncogéniques par des petites molécules de synthèse
Pernak, M. (2023) · doi:10.70675/f86abd5cz1e80z4ac3z8b7ez6eb76d625a54
Computational fragment-based design of chemically modified oligonucleotides for selective protein inhibition : BACE1 as a case study
González‐Alemán, R. (2023) · theses.fr (ABES) · doi:10.70675/d579a173zeee9z409fzaf5ez64ae20859f75
Advanced computational techniques to aid the rational design of small molecules targeting RNA
Panei, F. P. (2024) · theses.fr (ABES) · doi:10.70675/f278a628z9cf1z4408za15fzfed05518864e
Rational design of bioactive molecules for antibiotic purposes
Imbernon, J. R. (2023) · theses.fr (ABES) · doi:10.70675/ff9e5fd3za154z4b07zb082z0544574f7124
Avogadro–Scaled Hypergeometric–Chebyshev Feature Maps and Entanglement-Inspired Kernels for Generative-Docking Pattern Recognition in SARS-CoV-2 TargetsPreprint
GRIGORIADIS, I. (2026) · Research Square · doi:10.21203/rs.3.rs-9361450/v1
Integration of AI-Driven Drug Design in Industry and Academia
Gordillo, J. A. G., García, L. J. H., & Núñez, C. A. G. (2026) · Springer eBooks · doi:10.1007/978-3-032-20428-8_10
Identification of AMPD2 allosteric inhibitors with novel mechanism of action by fragment merging approach
Yamanaka, K., Uhara, T., Nomura, A., Akaki, T., Adachi, T., Kitao, Y., & Hantani, Y. (2026) · SLAS DISCOVERY · doi:10.1016/j.slasd.2026.100313
Rapid Fragment Screening by 19 F Steady‐State Free Precession NMR
Rudusa, L., Ozola, S., Melnykov, K. P., Filatov, Y. I., Ryabukhin, S. V., Bobrovs, R., Volochnyuk, D. M., Jaudzems, K., & Aleksis, R. (2026) · Angewandte Chemie · doi:10.1002/ange.6891540
From Genome Inspection to Precision Agrochemicals: A Structure-Based Antivirulence Roadmap for Sustainable Crop Protection against Xylella fastidiosa
Caliandro, R., Astolfi, A., Amoia, S. S., Belviso, B. D., Mangini, V., Neto, R. R. D. S., Sciancalepore, A. G., Sabatini, S., Giampetruzzi, A., Barreca, M. L., et al. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c16967
Rapid Fragment Screening by 19 F Steady‐State Free Precession NMR
Rudusa, L., Ozola, S., Melnykov, K. P., Filatov, Y. I., Ryabukhin, S. V., Bobrovs, R., Volochnyuk, D. M., Jaudzems, K., & Aleksis, R. (2026) · Angewandte Chemie International Edition · doi:10.1002/anie.6891540
High-Throughput Screening Strategies in Microbial Studies
Sobti, R. C., & Khan, M. A. (2026) · Springer eBooks · doi:10.1007/978-981-95-8840-4_7
ML-Based Classification of Druggable Binding Sites Integrating AlphaFold Confidence MetricsPreprint
Marimuthu, N., Aarsha, R. K., Paruchuri, A., Dakshinamurthy, S., & Mahendran, R. (2026) · Research Square · doi:10.21203/rs.3.rs-9676675/v1
Computationally driven design and validation of piperazine-bridged phthalimide derivatives as multifunctional agents against Alzheimer's disease
Devi, B., Jangid, K., Temgire, P., Kumar, N., Thareja, S., Kumar, P., & Kumar, V. (2026) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2026.119417
Room-temperature fragment screening of soluble epoxide hydrolase by serial crystallographyPreprint
Dunge, A., Wehlander, G., Brändén, G., & Käck, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.01.729266
Thermodynamic profiling and fragment screening of GPCRs using grating-coupled interferometry
Churchill-Angus, A. M., Crespillo, S., Pascualli, E. I. G., Bertinelli, M., Grou, C., Errey, J. C., Fiez-Vandal, C., & Fabini, E. (2026) · SLAS DISCOVERY · doi:10.1016/j.slasd.2026.100314
A Drug–Target Specificity Foundation Model for Off-target Prediction, Repurposing, and Generative DesignPreprint
Reddy, S. T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.08.730844
Integrating 19 Focused Screening with Make‐On‐Demand Chemical Spaces for Enhanced Fragment Follow‐Up
Penner, P., Henry, C., Schröder, M., & Vulpetti, A. (2026) · ChemMedChem · doi:10.1002/cmdc.70333 · Cited by 1
Phenotype-first covalent fragment screening identifies a synthetic lethal TYMS inhibitor in ATRX-deficient cellsPreprint
Raguseo, F., Fellows, E., de Chiara, C., Mortishire-Smith, B. J., Segura‐Bayona, S., Cawood, E. E., Jiang, M., Subtil, F. T., Idilli, A. I., McCarthy, W. J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.10.731339
Advances in structure-based drug design: Principles and case studies in therapeutic development
Prasad, S. V. V. S., & Bairagya, H. R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49253-2.00021-0
DiffDesign–An SE(3)-Equivariant Diffusion Model for Molecule Linker Generation
Truong, T. H., Lee, J. H., & Yeom, M. S. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-7896-2_2
The Potential of AI-Driven High-Content Imaging in Drug Discovery
Ali, R., & Tamboli, Y. (2026) · ACS Omega · doi:10.1021/acsomega.5c12320
Streamlined Scalable Synthesis of 4-Bromo-6-chloro-5-iodo-1-(tetrahydro-2 H -pyran-2-yl)-1 H -indazole: A Building Block for KRAS Inhibitors
Zhu, X., Xue, S., Wang, W., Zhang, X., Cui, Z., Chen, Z. F., & Debnath, S. (2026) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.6c01293
Discovery of Noninhibitory Macrocyclic Ligands for Protein Tyrosine Phosphatase 1B Using a Function-Based, Iterative Screening Strategy
Dong, J., Li, B., Fu, C., & Kodadek, T. J. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.6c07205
Discovery of low molecular weight protein-binding fragments from bead-displayed libraries: application to monomeric proteins
Tong, J., Jayalath, I. M., Parsons, J. V., Kim, J.-A., Mann, M. D., Tokmina‐Roszyk, M., & Kodadek, T. J. (2026) · RSC Chemical Biology · doi:10.1039/d6cb00128a
Lead Discovery
Silverman, R. B., Xue, F., & Juncosa, J. I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-15862-9.00002-3
Beyond the ATP-binding pocket: emerging strategies in kinase targeting from allosteric inhibition to targeted protein degradation
Zhou, M., Li, L., Tang, X., Zhao, Y., & Zhang, W. (2026) · Future Medicinal Chemistry · doi:10.1080/17568919.2026.2718758
Vertical Multi-Target Design of Potential Staphylococcus aureus Respiratory Inhibitors: A Systematic in Silico Fragment-Based Approach
Srikanth, S., Alexandar, S. P., & Ulaganathan, V. (2026) · OMICS A Journal of Integrative Biology · doi:10.1177/15578100261479161
Multidisciplinary Approaches to Heterocyclic Oncology Research
Goti, V., Verma, A., & Armo, R. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898817084126010014
Principal Component Analysis Based Deconvolution of NMR Chemical Shifts for Objective Biomolecular Interaction Mapping
Yang, M. J., Choi, J., Lee, H., Kang, M., Heo, J., Xiang, S., Park, C., Jeon, Y. H., Cheong, H., Ryu, K., et al. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.6c02780
Direct Molecular Editing of Complex Molecules for Drug Discovery
Cheng, S., Ma, C., Chang, X., Chang, J., & Yu, B. (2026) · Angewandte Chemie · doi:10.1002/ange.3399600
AI-powered medicinal chemistry and translational drug development
Kaicheng, U., Zhang, S. M., Yu, Z., Zhang, Z., Zhang, J., He, C., Liu, A., Chen, R., Wang, S., Yan, L., et al. (2026) · Chemical Society Reviews · doi:10.1039/d5cs01469g
Dynamic Protein Structure Paradox: An Integrative Framework for Endpoint-Conditioned Evidentiary Sufficiency in Structure-to-Function Claims
Niazi, S. K. (2026) · Cells · doi:10.3390/cells15171560
“Fragment-based discovery uses the structures of weak binders to guide processes such as growth, merging, and linking, thereby enabling the generation of clinical compounds from initial points too weak to be identified through conventional screening methods [80].”
Structure prediction and drug screening targeting monkeypox virus polymerase and surface proteins
Tao, T., Jia, L., Cheng, Y., Zou, Y., Wen, Y., Chen, Y., Chen, H., Wei, H., Yang, Q., & Shi, Y. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00937-9
“These preliminary binding responses provide a basis for further affinity optimization and orthogonal validation [37].”
Structural biology of the dengue virus NS2B–NS3 protease as a target for antiviral drug development
Paul, R., Jyothi, D., Mukerjee, M., Chatterjee, S., Manna, P., & Pal, P. C. (2026) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2026.110485
Synthesis and Antifungal Evaluation of Isoquinoline‐3‐amide Derivatives Containing a Phenoxypyridine Moiety for Plant Protection
Zhang, J., Sun, S., Guo, Y., Xu, Z., Zhang, J., Feng, G., Ding, H., & Xiao, Q. (2026) · Chemistry & Biodiversity · doi:10.1002/cbdv.71635
“To find effective active fragments and create new pesticides by using intermediate derivatization method or active substructure splicing is beneficial to pesticide innovation and reduce research costs [4, 5].”
The Scope Concept: Defined Chemical Space for Identifying Ligand-Efficient, Tractable Hits Applied to Drug Discovery Targets Trypanosomal CLK1, 14–3–3/Estrogen Receptor-α, and SARS-CoV-2 Mpro
Jansen, J. M., Cortopassi, W., Fuller, D., Ertl, P., Fang, E., Gupta, R. R., Jia, W., Jiřiček, J., Knapp, M., Lingel, A., et al. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01654
FS-DTI: Predicting drug–target interactions by linking molecular fragments to protein subpocketsPreprint
Wang, L., Xiao, J., Shi, X., Xu, J., & Chen, C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7511806
Targeting the IL-17A Cytokine Interface: Structural Insights and Small-Molecule Modulators
Kim, G. U., Lee, A., Lee, S., Jeon, B., Park, H., Kang, T., & Choo, H. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c01296
Automated High-Throughput Ligand-Detected NMR for Medicinal Chemistry Hit Triage and Binding Assessment
Falk, B. T., Mueller, L., Braunstein, E., Rivera, J., Jorge, C., Khandelwal, P., & Caceres‐Cortes, J. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c01749
Fragment-Based Design of Pesticide-Like Molecules: From Method Development to Agrochemical Discovery
Shi, X.-X., Wang, Z.-Z., Yang, R., Wang, F., & Yang, G. (2026) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6c00580
MolecureAI: an agentic AI platform for autonomous drug discovery
Chadi, M.-A., Asmaa, A., Hajar, E., & Mousannif, H. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1917774
Fragment-based drug discovery strategy reveals an unprecedented allosteric binding site in Mycobacterium tuberculosis DNA gyrase
Shiroma, S. T., Rossini, N. O., Armenia, J. F., Silva, C. D. S., Souza, P. C. T., Coureux, P., Osheroff, N., & Rossini, N. D. O. (2026) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2026.119380
Deciphering the Structure−Activity Relationships Underpinning the Linkerology of Bivalent EGFR Inhibitors
Patel, K. B., Akinwumi, I. A., Weber, T. M., MacDonald, M. R., Collins, S. J., Chitnis, S. P., Zack, C. L., Ogboo, B. C., Nasief, N. N., Ouellette, E. A., et al. (2026) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6c02714
CASyM: Chemotype Annotation through Synthesis Mapping
Torren-Peraire, P., Verhoeven, J., Herman, D., Ceulemans, H., Tetko, I. V., & Wegner, J. K. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00645
AlphaFold-driven structure-based drug discovery: Capabilities, benchmarks and limitations for medicinal chemistry
Pathak, M. (2026) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2026.119442 · Cited by 1
BindingDB Entry 50019000: Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design.
BindingDB (2024) · UC San Diego · doi:10.7270/q2rx9hbv
BindingDB Entry 50047928: Fragment-to-Lead Medicinal Chemistry Publications in 2015.
BindingDB (2018) · UC San Diego · doi:10.7270/q2r78h5b
BindingDB Entry 50009766: Design, synthesis and algicides activities of thiourea derivatives as the novel scaffold aldolase inhibitors.
BindingDB (2021) · UC San Diego · doi:10.7270/q20p13hv
BindingDB Entry 50013495: Dual Bcl-X
BindingDB (2022) · UC San Diego · doi:10.7270/q2t157dk
BindingDB Entry 50006895: Discovery of novel allosteric site and covalent inhibitors of FBPase with potent hypoglycemic effects.
BindingDB (2021) · UC San Diego · doi:10.7270/q2rb77w3
BindingDB Entry 50014992: Fragment-Based Design of a Potent MAT2a Inhibitor and
BindingDB (2023) · UC San Diego · doi:10.7270/q2rr234t
BindingDB Entry 50000152: Fragment-Based Discovery and Optimization of Enzyme Inhibitors by Docking of Commercial Chemical Space.
BindingDB (2019) · UC San Diego · doi:10.7270/q2pv6njr
BindingDB Entry 50020161: Advances of bioorthogonal coupling reactions in drug development.
BindingDB (2024) · UC San Diego · doi:10.7270/q2th8s34
BindingDB Entry 50000760: Allosteric Targeting of the Fanconi Anemia Ubiquitin-Conjugating Enzyme Ube2T by Fragment Screening.
BindingDB (2020) · UC San Diego · doi:10.7270/q2z89fwh
BindingDB Entry 50008819: Optimization of novel reversible Bruton's tyrosine kinase inhibitors identified using Tethering-fragment-based screens.
BindingDB (2021) · UC San Diego · doi:10.7270/q2tx3jsf
BindingDB Entry 50003274: In silico fragment-based drug design with SEED.
BindingDB (2020) · UC San Diego · doi:10.7270/q26w9drp
BindingDB Entry 50001714: Non-covalent Small-Molecule Kelch-like ECH-Associated Protein 1-Nuclear Factor Erythroid 2-Related Factor 2 (Keap1-Nrf2) Inhibitors and Their Potential for Targeting Central Nervous System Diseases.
BindingDB (2020) · UC San Diego · doi:10.7270/q2cj8h17
BindingDB Entry 50001811: Discovery of potent and selective BRD4 inhibitors capable of blocking TLR3-induced acute airway inflammation.
BindingDB (2020) · UC San Diego · doi:10.7270/q2x069km
BindingDB Entry 50016012: Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking.
BindingDB (2023) · UC San Diego · doi:10.7270/q2d222n4
BindingDB Entry 50019683: Rapid Elaboration of Fragments into Leads Applied to Bromodomain-3 Extra-Terminal Domain.
BindingDB (2024) · UC San Diego · doi:10.7270/q23t9nkq

Authors: Erlanson, D. A., Fesik, S. W., Hubbard, R. E., Jahnke, W., & Jhoti, H.
Affiliations: Carmot Therapeutics (United States) (US)
Vanderbilt University (US)
Vernalis (R&D) Limited (United Kingdom) (GB)
Novartis (Switzerland) (CH)
Corresponding author: Erlanson, Daniel A.
ORCID: Erlanson, Daniel A.
Fesik, Stephen W.
Hubbard, Roderick E.
Jahnke, Wolfgang
Jhoti, Harren
Venue: Nature Reviews Drug Discovery
Publisher: Springer Science and Business Media LLC
Published: 2016-07-15
Volume: 15
Issue: 9
Pages: 605-619
ISSN: 1474-1776 (ISSN-L)
1474-1784
DOI: 10.1038/nrd.2016.109
OpenAlex: W2467779447
Cited by: 1,000
References: 110
Citations by year: 2026: 80 · 2025: 92 · 2024: 112 · 2023: 95 · 2022: 97 · 2021: 106 · 2020: 141 · 2019: 79 · 2018: 99 · 2017: 84 · 2016: 14
FWCI: 7.35
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Reviews Drug Discovery (publishedVersion)
PubMed (publishedVersion)
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: fragment-based drug discovery, fragment libraries, fragment screening
MeSH terms: Clinical Trials as Topic, Humans, Peptide Fragments, Protein Conformation, Peptide Library, Computational Biology, Small Molecule Libraries, Drug Discovery, High-Throughput Screening Assays

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

Twenty years on: the impact of fragments on drug discovery

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks