Why do we need so many chemical similarity search methods?

The Protein Data Bank

Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196

Chemical Similarity Searching

Willett, P., Barnard, J. M., & Downs, G. M. (1998) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci9800211 · Cited by 1,775

Concepts and applications of molecular similarity

Johnson, M. A., & Maggiora, G. M. (1990) · Wiley eBooks · Cited by 1,301

Atom pairs as molecular features in structure-activity studies: definition and applications

Carhart, R. E., Smith, D. H., & Venkataraghavan, R. (1985) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00046a002 · Cited by 1,101

Virtual screening—an overview

Walters, W. P., Stahl, M. T., & Murcko, M. A. (1998) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01163-x · Cited by 996

Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins.

Charifson, P. S., Corkery, J. J., Murcko, M. A., & Walters, W. P. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm990352k · Cited by 698

Use of Structure−Activity Data To Compare Structure-Based Clustering Methods and Descriptors for Use in Compound Selection

Brown, R. D., & Martin, Y. C. (1996) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci9501047 · Cited by 631

Neighborhood Behavior: A Useful Concept for Validation of “Molecular Diversity” Descriptors

Patterson, D. E., CRAMER, R. D. I., Ferguson, A. M., Clark, R. D., & Weinberger, L. E. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm960290n · Cited by 509

The Information Content of 2D and 3D Structural Descriptors Relevant to Ligand-Receptor Binding

Brown, R. D., & Martin, Y. C. (1997) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci960373c · Cited by 378

New 4-Point Pharmacophore Method for Molecular Similarity and Diversity Applications: Overview of the Method and Applications, Including a Novel Approach to the Design of Combinatorial Libraries Containing Privileged Substructures

Mason, J. S., Morize, I., Menard, P. R., Cheney, D. L., Hulme, C., & Labaudinière, R. F. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm9806998 · Cited by 375

Neural Network Models for Prediction of Biological Activity using Molecular Dynamics Data: A Case of Photoswitchable Peptides

Cherednichenko, A., Afonin, S., Babii, O., Voitsitskyi, T., Stratiichuk, R., Koleiev, I., Vozniak, V., Shevchuk, N., Ostrovsky, Z., Yesylevskyy, S. O., et al. (2025) · Molecular Informatics · doi:10.1002/minf.70001

qMol: A Web Server for Efficient Molecular Queries Using Fragment-Based Reduced Graphs

Zhou, W., Ke, D., Zhang, Z., Jin, C., Wu, Y., Pan, X., Wang, X., Xiao, X., Zhang, K., & Ji, C. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02195

Inference bayesian networks for molecular database similarity searching

Salim, N. B. (2008)

Few-Shot Learning in Drug Discovery

Schimunek, J., & Klambauer, G. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_16 · Cited by 1

Les triplets pharmacophoriques flous : développement et applications

Bonachéra, F. (2011) · theses.fr (ABES) · doi:10.70675/f0cedbe1z6c18z48e4z901cza22a3ae4e98a

Développement d’outils statistiques d’évaluation de méthodes de criblage virtuel : courbes de prédictivité & Screening Explorer

Empereur-mot, C. (2017) · theses.fr (ABES) · doi:10.70675/2b288725z2446z48c5z833ezf53915cd324b

3DOpt: Benchmark for Automated Design of 3D Molecular Structures across the Periodic Table

Costamagna, M., Thomas, M., Foscato, M., & Jensen, V. R. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00259

Discovery of potent VEGFR2 inhibitors as anticancer target, using pharmacophore based virtual screening, molecular docking and molecular dynamics simulation

Meziani, O., Ferkous, F., Kaki, S. A., Kraim, K., Saihi, Y., Mohammed, M. M. D., & Osman, A. M. A. (2026) · Chemical Papers · doi:10.1007/s11696-026-05026-7

Count your bits: fingerprint benchmarking to assess broad chemical space representation

Huber, F., & Pollmann, J. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01245-y · Cited by 8

Lead Discovery

Silverman, R. B., Xue, F., & Juncosa, J. I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-15862-9.00002-3

From cheminformatics to large language models: emerging paradigms in food chemical toxicity prediction

Suvedha, B., Abhishek, Eshan, Chaturvedi, N., & Kumar, V. (2026) · Future Foods · doi:10.1016/j.fufo.2026.101174

Machine Learning‐Aided Small‐Molecule Virtual Screening: Recent Advances and Future Perspectives

Wang, Y., Bansal, N., Wa, S., Sciabola, S., & Wang, Y. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70084

BindingDB Entry 50031858: Elaborate ligand-based pharmacophore exploration and QSAR analysis guide the synthesis of novel pyridinium-based potent beta-secretase inhibitory leads.

BindingDB (2010) · UC San Diego · doi:10.7270/q2zg6sfr

BindingDB Entry 50010960: Learning Molecular Representations for Medicinal Chemistry.

BindingDB (2021) · UC San Diego · doi:10.7270/q2765jxg

BindingDB Entry 50034407: Elaborate ligand-based modeling and subsequent synthetic exploration unveil new nanomolar Ca2+/calmodulin-dependent protein kinase II inhibitory leads.

BindingDB (2012) · UC San Diego · doi:10.7270/q2959j05

BindingDB Entry 50015046: A virtual screening approach to finding novel and potent antagonists at the melanin-concentrating hormone 1 receptor.

BindingDB (2009) · UC San Diego · doi:10.7270/q2668cp3

BindingDB Entry 50045277: Discovery of nanomolar phosphoinositide 3-kinase gamma (PI3K¿) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis.

BindingDB (2016) · UC San Diego · doi:10.7270/q2057hm8

BindingDB Entry 50032548: Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs.

BindingDB (2011) · UC San Diego · doi:10.7270/q2s182rt

BindingDB Entry 50040348: Symmetric kv1.5 blockers discovered by focused screening.

BindingDB (2013) · UC San Diego · doi:10.7270/q2st7qxg

BindingDB Entry 50038206: Pharmacophore modeling, quantitative structure-activity relationship analysis, and in silico screening reveal potent glycogen synthase kinase-3beta inhibitory activities for cimetidine, hydroxychloroquine, and gemifloxacin.

BindingDB (2012) · UC San Diego · doi:10.7270/q20z744c

BindingDB Entry 50034066: A novel potential therapeutic avenue for autism: design, synthesis and pharmacophore generation of SSRIs with dual action.

BindingDB (2012) · UC San Diego · doi:10.7270/q23b60jf

BindingDB Entry 50037568: Virtual screening workflow development guided by the"receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4.

BindingDB (2012) · UC San Diego · doi:10.7270/q23x87fm

BindingDB Entry 1890: Identification of chemically diverse Chk1 inhibitors by receptor-based virtual screening.

BindingDB (2007) · UC San Diego · doi:10.7270/q2s46q6s

BindingDB Entry 50027240: Combining ligand-based pharmacophore modeling, quantitative structure-activity relationship analysis and in silico screening for the discovery of new potent hormone sensitive lipase inhibitors.

BindingDB (2010) · UC San Diego · doi:10.7270/q23778kj

BindingDB Entry 50033750: Ligand-based modelling followed by synthetic exploration unveil novel glycogen phosphorylase inhibitory leads.

BindingDB (2012) · UC San Diego · doi:10.7270/q2028rxj

BindingDB Entry 50038090: Discovery of new MurF inhibitors via pharmacophore modeling and QSAR analysis followed by in-silico screening.

BindingDB (2012) · UC San Diego · doi:10.7270/q2vx0hct

BindingDB Entry 50015214: Novel cyclooxygenase-1 inhibitors discovered using affinity fingerprints.

BindingDB (2009) · UC San Diego · doi:10.7270/q24x578j

Similarity searching

Stumpfe, D., & Bajorath, J. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.23 · Cited by 126

Applications of Information Retrieval Methods in Computer-Aided Drug Discovery

Willett, P. (2001) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 1

Comparison of Similarity Method to Improve Retrieval Performance for Chemical Data

Nurjana, N. R., & Zainudin, S. (2018) · Asia-Pacific Journal of Information Technology and Multimedia · doi:10.17576/apjitm-2018-0701-08 · Cited by 2

Chemical similarity searches: when is complexity justified?

Sheridan, R. P. (2007) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2.4.423 · Cited by 49

Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents

Laufkötter, O., Miyao, T., & Bajorath, J. (2019) · ACS Omega · doi:10.1021/acsomega.9b02470 · Cited by 10