Publications

In total, by now almost 4000 publications have used or cited LigandScout technology in different areas of computer-aided molecular design, starting from hit identification, to hit to lead, and lead optimization up to fragment-based discovery, drug repurposing and target prediciton.

For an up-to-date list of publications, check Google Scholar.

LigandScout XT

Application

  • Doijen J, Xie J, Marsili S, Bains T, Mann MK, Abeywickrema P, Van den Broeck N, Permann C, Langer T, Ibis G, Mattelaer CA, Harvey J, van Raalte S, Fino R, Pande V, Peeters D, Patrick A, Van Damme E, van Vlijmen H, Van Loock M, Jacoby E. A New Fragment-Based Pharmacophore Virtual Screening Workflow Identifies Potent Inhibitors of SARS-CoV-2 NSP13 Helicase. J Comput Chem. 2025; 5;46(23): e70201. DOI: 10.1002/jcc.70201.
  • Kalaba, P, Perisic Böhm, M, D̵ikić, F, Tomašević, N, Drdla-Schutting, R, Hasinger, S, Ines, DM, Wolf, A, Belil-Catalina, M, Bryant, SD, Spetea, M, Gruber, CW, Muttenthaler, NM and Keimpema, E. Development and Biological Characterization of Fluorescent Dynorphins for the Visualization of Kappa Opioid Receptors. J. Med. Chem. 2026, J. Med. Chem. 2026. DOI: 10.1021/acs.jmedchem.5c03072

LigandScout

Technology

  • Wolber, G.; Langer, T.; LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters J. Chem. Inf. Model; 2005; 45(1); 160-169. DOI: 10.1021/ci049885e
  • Wolber, G.; Dornhofer, A. A.; Langer, T.; Efficient overlay of small organic molecules using 3D pharmacophores J. Comput. Aided Mol. Des.; 2007; 20(12); 773-788. DOI: 10.1007/s10822-006-9078-7
  • Kainrad T., Hunold S., Seidel T., Langer T.; LigandScout Remote: A New User-Friendly Interface for HPC and Cloud Resources J. Chem. Inf. Model; 2018. DOI: 10.1021/acs.jcim.8b00716
  • Seidel, T., Bryant, S.D., Ibis, G., Poli, G. and Langer, T. (2017). 3D Pharmacophore Modeling Techniques in Computer-Aided Molecular Design Using LigandScout. In Tutorials in Chemoinformatics, A. Varnek (Ed.). DOI: 10.1002/9781119161110.ch20

Application

  • Karaboga, A. S.; Planesas, J. M.; Petronin, F.; Teixido, F; Souchet, M.; Perez-Nueno, I. V. Highly Specific and Sensitive Pharmacophore Model for Identifying CXCR4 Antagonists. Comparison with Docking and Shape-Matching Virtual Screening Performance Journal of Chemical Information and Modeling; 2013; 54, 1747-1757. DOI:10.1021/ci400037y
  • Millard M, Müldür KD, Kilian J, Mair V, Holzer W, Hacker M, Langer T, Ozenil M, Pichler V. Synthesis, in silico and in vitro evaluation of N-phenylbenzimidazole derivatives targeting muscarinic acetylcholine receptors. Bioorganic Chemistry, Vol.178, 2026, 109932. DOI: 10.1016/j.bioorg.2026.109932.
  • Dermawan D, Elbouamri L, Chtita S, Alotaiq N. Molecular Insights into Bromocriptine Binding to GPCRs Within Histamine-Linked Signaling Networks: Network Pharmacology, Pharmacophore Modeling, and Molecular Dynamics Simulation. Int J Mol Sci. 2025 Sep 7;26(17):8717. DOI: 10.3390/ijms26178717.
  • Tillmanns J, Battisti V, Kicuntod J, Hahn F, Obergfäll D, Geiger P, Wagner S, Buschmann H, Lesch B, Lischka P, Sticht H, Langer T, Marschall M. The conserved core nuclear egress complex (NEC) as an antiherpesviral drug target: Pharmacophore-based identification of NEC-specific inhibitors. Antiviral Research,Vol. 239, 2025,106168. DOI: 10.1016/j.antiviral.2025.106168.
  • Fellinger C, Seidel T, Merget B, Schleifer KJ, Langer T. GRADE and X-GRADE: Unveiling Novel Protein–Ligand Interaction Fingerprints Based on GRAIL Scores. Journal of Chemical Information and Modeling 2025 65 (5), 2456-2475.DOI: 10.1021/acs.jcim.4c01902.
  • Heider J, Kilian J, Garifulina A, Hering S, Langer T, Seidel T. Apo2ph4: A Versatile Workflow for the Generation of Receptor-based Pharmacophore Models for Virtual Screening. Journal of Chemical Information and Modeling 2023 63 (1), 101-110. DOI: 10.1021/acs.jcim.2c00814
  • Gallego, R.A., Bernier, L., Chen, H., Cho-Schultz, S., Chung, L., Collins, M., Del Bel, M., Elleraas, J., Costa Jones, C., Cronin, C.N. and Edwards, M., 2023. Design and synthesis of functionally active 5-amino-6-aryl pyrrolopyrimidine inhibitors of hematopoietic progenitor kinase 1. Journal of Medicinal Chemistry, 66(7), pp.4888-4909.
  • Culletta, G.; Tutone, M.; Ettari, R.; Perricone, U.; Di Chio, C.; Almerico, A.M.; Zappalà, M. Virtual Screening Strategy and In Vitro Tests to Identify New Inhibitors of the Immunoproteasome. Int. J. Mol. Sci. 2023, 24, 10504. DOI: 10.3390/ijms241310504
  • L.De Luca, A.Angeli, F.Ricci, C. T.Supuran, R.Gitto. Structure-guided identification of a selective sulfonamide-based inhibitor targeting the human carbonic anhydrase VA isoform. Arch. Pharm. 2023;356:e2200383. DOI: 10.1002/ardp.202200383.
  • Guzelj, S.; Tomašič, T.; Jakopin, Ž. Novel Scaffolds for Modulation of NOD2 Identified by Pharmacophore-Based Virtual Screening. Biomolecules 2022, 12, 1054. DOI: 10.3390/biom12081054
  • Yoshimori A, Asawa Y, Kawasaki E, Tasaka T, Matsuda S, Sekikawa T, Tanabe S, Neya M, Natsugari H, Kanai C. Design and Synthesis of DDR1 Inhibitors with a Desired Pharmacophore Using Deep Generative Models. ChemMedChem. 2021 Mar 18;16(6):955-958. DOI: 10.1002/cmdc.202000786.
  • Tomašič, T.; Durcik, M.; Keegan, B.M.; Skledar, D.G.; Zajec, Ž.; Blagg, B.S.J.; Bryant, S.D. Discovery of Novel Hsp90 C-Terminal Inhibitors Using 3D-Pharmacophores Derived from Molecular Dynamics Simulations. Int. J. Mol. Sci. 2020, 21, 6898. DOI: 10.3390/ijms21186898
  • Arthur G, Oliver W, Klaus B, Thomas S, Gökhan I, Sharon B, Isabelle T, Pierre D, Thierry L. Hierarchical Graph Representation of Pharmacophore Models. Front Mol Biosci. 2020 Dec 14;7:599059. doi: 10.3389/fmolb.2020.599059.
  • Krautscheid, Y.; Ake Senning, C. J.; Sartori, S. B.; Singewald, N.; Schuster, D.; Stuppner, H. Pharmacophore Modeling, Virtual Screening, and in Vitro Testing Reveal Haloperidol, Eprazinone, and Fenbutrazate as Neurokinin Receptors Ligands Journal of Chemical Information and Modeling; 2014; 152, 838-854.
    DOI:10.1021/ci500106z
  • Wei, Y.; Ma, Y.; Zhao, Q.; Ren, Z.; Li, Y.; Hou, T.; Peng, H. New Use for an Old Drug: Inhibiting ABCG2 with Sorafenib Molecular Cancer Therapeutics; 2012; 11, 1693-1702. DOI:10.1158/1535-7163.MCT-12-0215

LigandScout Publications involving Natural Products

There are nearly 600 publications involving NPs and LigandScout. Here are a few.

  • Baei B, Askari P, Askari FS, Kiani SJ, Mohebbi A. Pharmacophore modeling and QSAR analysis of anti-HBV flavonols. PLoS One. 2025 Jan 13;20(1):e0316765. doi: 10.1371/journal.pone.0316765.
  • Abdulrahman M. Eid, Abdallah Selim, Mohamed Khaled, and Abdo A. Elfiky. Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5. The Journal of Physical Chemistry B. 2024 128 (29), 7086-7101. DOI: 10.1021/acs.jpcb.4c02083.
  • Tjiptaningrum A, Kurniati I, Fadilah F, Susantiningsih T, Prawiningrum AF, Utari WD, Erlina L. Protein Interaction Analysis and Molecular Simulation of the Anti-Inflammatory Activities in Melaleuca cajuputi Extract Against COVID-19. Int J Inflam. 2024 Nov 28;2024:5568294. DOI: 10.1155/ijin/5568294.
  • Preet G, Astakala RV, Gomez-Banderas J, Rajakulendran JE, Hasan AH, Ebel R, Jaspars M. Virtual Screening of a Library of Naturally Occurring Anthraquinones for Potential Anti-Fouling Agents. Molecules. 2023 Jan 19;28(3):995. DOI: 10.3390/molecules28030995.
  • Wasilewicz A, Kirchweger B, Bojkova D, Abi Saad MJ, Langeder J, Bütikofer M, Adelsberger S, Grienke U, Cinatl J Jr, Petermann O, Scapozza L, Orts J, Kirchmair J, Rabenau HF, Rollinger JM. Identification of Natural Products Inhibiting SARS-CoV-2 by Targeting Viral Proteases: A Combined in Silico and in Vitro Approach. J Nat Prod. 2023 Feb 24;86(2):264-275. DOI: 10.1021/acs.jnatprod.2c00843.
  • Seidel T, Wieder O, Garon A, Langer T. Applications of the Pharmacophore Concept in Natural Product inspired Drug Design. Mol Inform. 2020 Nov;39(11):e2000059. DOI: 10.1002/minf.202000059.
  • Karaman Mayack B, Sippl W, Ntie-Kang F. Natural Products as Modulators of Sirtuins. Molecules. 2020 Jul 20;25(14):3287. DOI: 10.3390/molecules25143287.

IL PharmDB

Selected Application Articles on Target Prediction and Activity Profiling.

  • Mayr F, Möller G, Garscha U, Fischer J, Rodríguez Castaño P, Inderbinen SG, Temml V, Waltenberger B, Schwaiger S, Hartmann RW, Gege C, Martens S, Odermatt A, Pandey AV, Werz O, Adamski J, Stuppner H, Schuster D. Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction. Int J Mol Sci. 2020 Sep 26;21(19):7102. DOI: 10.3390/ijms21197102.
  • Steindl, T. M; Schuster, Laggner, C.; Chuang, K.; Hoffmann, R.; Langer, T.; Parallel Screening and Activity Profiling with HIV Protease Inhibitor Pharmacophore Models J. Chem. Inf. Model; 2007; 47(2); 563-571.
    DOI: 10.1021/ci600321m
  • Schuster, D.; Laggner, C.; Steindl, T. M.; Langer, T.; Development and validation of an in silico P450 profiler based on pharmacophore models. Curr. Drug Discov. Technol.; 2006; 3(1); 1-48.
  • Steindl, T. M; Schuster, D.; Laggner, C.; Langer, T.; Parallel screening: a novel concept in pharmacophore modeling and virtual screening J. Chem. Inf. Model; 2006; 46(5); 2146-2157. DOI: 10.1021/ci6002043

IL NeuroDeRisk Toolbox

  • GABA_A Receptor Seizure Liabilities: Bampali, K., et al. (2023). GABAA receptor-mediated seizure liabilities: a mixed-methods screening approach. Cell Biology and Toxicology, 39, 2793-2819. DOI: 10.1007/s10565-023-09803-y.
  • Kohlbacher SM, Langer T, Seidel T. QPHAR: quantitative pharmacophore activity relationship: method and validation. J Cheminform. 2021 Aug 9;13(1):57. DOI: 10.1186/s13321-021-00537-9.