Recognizing Pitfalls in Virtual Screening: A Critical Review
Abstract
The aim of virtual screening (VS) is to identify bioactive compounds through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large number of molecules are ranked according to their likelihood to be bioactive compounds, with the aim to enrich the top fraction of the resulting list (which can be tested in bioassays afterward). At its core, VS attempts to improve the odds of identifying bioactive molecules by maximizing the true positive rate, that is, by ranking the truly active molecules as high as possible (and, correspondingly, the truly inactive ones as low as possible). In choosing the right approach, the researcher is faced with many questions: where does the optimal balance between efficiency and accuracy lie when evaluating a particular algorithm; do some methods perform better than others and in what particular situations; and what do retrospective results tell us about the prospective utility of a particular method? Given the multitude of settings, parameters, and data sets the practitioner can choose from, there are many pitfalls that lurk along the way which might render VS less efficient or downright useless. This review attempts to catalogue published and unpublished problems, shortcomings, failures, and technical traps of VS methods with the aim to avoid pitfalls by making the user aware of them in the first place.
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Kundu, B., & Guptasarma, P. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19991115)37:3 · Cited by 39 · see citing works
Izrailev, S., Zhu, F., & Agrafiotis, D. K. (2006) · Journal of Computational Chemistry · doi:10.1002/jcc.20506 · Cited by 35 · see citing works
Self-citationRohrer, S. G., & Baumann, K. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700099u · Cited by 34 · see citing works
Yongye, A. B., Bender, A., & Martínez‐Mayorga, K. (2010) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-010-9365-1 · Cited by 33 · see citing works
Self-citationComputation of 3D queries for ROCS based virtual screens
Tawa, G. J., Baber, J. C., & Humblet, C. C. (2009) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-009-9302-3 · Cited by 31 · see citing works
López-Vallejo, F., Nefzi, A., Bender, A., et al. (2011) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2011.01100.x · Cited by 30 · see citing works
Self-citationLanger, T., & Wolber, G. (2004) · Pure and Applied Chemistry · doi:10.1351/pac200476050991 · Cited by 30 · see citing works
Self-citationConformational Sampling for Large-Scale Virtual Screening: Accuracy versus Ensemble Size
Griewel, A., Kayser, O., Schlosser, J., & Rarey, M. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9002415 · Cited by 29 · see citing works
Sheridan, R. P. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700380x · Cited by 24 · see citing works
Tresadern, G., & Agrafiotis, D. K. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9001926 · Cited by 15 · see citing works
Self-citationSteindl, T. M., & Langer, T. (2005) · QSAR & Combinatorial Science · doi:10.1002/qsar.200430929 · Cited by 15 · see citing works
Self-citationDevelopment of a New Pharmacophore Model That Discriminates Active Compstatin Analogs
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106 · see citing works
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Self-citationFrom machine learning to deep learning: progress in machine intelligence for rational drug discovery
Zhang, L., Tan, J., Han, D., & Zhu, H. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.08.010 · Cited by 760 · see citing works
Role of computer-aided drug design in modern drug discovery
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Entropy-Enthalpy Compensation: Role and Ramifications in Biomolecular Ligand Recognition and Design
Chodera, J. D., & Mobley, D. L. (2013) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-083012-130318 · Cited by 531 · see citing works
Molecular generative model based on conditional variational autoencoder for de novo molecular design
Lim, J., Ryu, S., Kim, J. W., & Kim, W. Y. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0286-7 · Cited by 421 · see citing works
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 · see citing works
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 · see citing works
Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00153 · Cited by 107 · see citing works
- Authors
- Scior, T., Bender, A., Tresadern, G., Medina-Franco, J. L., Martínez-Mayorga, K., Langer, T., Cuanalo-Contreras, K., & Agrafiotis, D. K.
- Affiliations
- Pharmacy Department, Facultad de Ciencias Químicas, Universidad Autónoma de Puebla, Puebla, Pue, México
- University of Cambridge (GB)
- Research Informatics & Integrative Genomics, Janssen Research & Development, Calle Jarama 75, Poligono Industrial, Toledo 45007, Spain
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
- Prestwick Chemical, Blvd Gonthier dʼAndernach, F-67400 Illkirch, France
- Janssen Research & Development (United States) (US)
- Corresponding author
- Scior, Thomas
- ORCID
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-04-06
- Volume
- 52
- Issue
- 4
- Pages
- 867-881
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2008732224
- Cited by
- 438
- References
- 124
- Citations by year
- 2026: 18 · 2025: 22 · 2024: 29 · 2023: 29 · 2022: 30 · 2021: 34 · 2020: 29 · 2019: 38 · 2018: 33 · 2017: 23 · 2016: 24 · 2015: 34 · 2014: 42 · 2013: 42 · 2012: 11
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- virtual screening, structure-based virtual screening, ligand-based virtual screening, bioactive compound identification, diagnostic sensitivity, method comparison
- MeSH terms
- Algorithms, Binding Sites, Humans, Ligands, Protein Binding, Proteins, Structure-Activity Relationship, User-Computer Interface, Likelihood Functions, Small Molecule Libraries, High-Throughput Screening Assays, Molecular Docking Simulation, 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.
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