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

Ligand-Based Virtual Screening Using Bayesian Inference Network and Reweighted Fragments

Ahmed, A., Abdo, A., & Salim, N. B. (2012) · The Scientific World JOURNAL · doi:10.1100/2012/410914 · Cited by 21

BRUTUS: Optimization of a grid-based similarity function for rigid-body molecular superposition. II. Description and characterization

Ronkko, T. P., Tervo, A. J., Parkkinen, J. P. S., & Poso, A. (2006) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9052-4 · Cited by 21

IADE: a system for intelligent automatic design of bioisosteric analogs

Ertl, P., & Lewis, R. A. (2012) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-012-9609-3 · Cited by 21

Consensus Models of Activity Landscapes

Medina‐Franco, J. L., Yongye, A. B., & López-Vallejo, F. (2012) · Wiley eBooks · doi:10.1002/9783527645121.ch11 · Cited by 21

Retrospective group fusion similarity search based on eROCE evaluation metric

Avram, S. I., Crisan, L., Bora, A., Pacureanu, L. M., Avram, Ș., & Kurunczi, L. (2013) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2012.12.041 · Cited by 20

Virtual Extensive Read-Across: A New Open-Access Software for Chemical Read-Across and Its Application to the Carcinogenicity Assessment of Botanicals

Viganò, E. L., Colombo, E., Raitano, G., Manganaro, A., Sommovigo, A., Dorne, J. L. C., & Benfenati, E. (2022) · Molecules · doi:10.3390/molecules27196605 · Cited by 20

Mapping of Drug-like Chemical Universe with Reduced Complexity Molecular Frameworks

Kontijevskis, A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00006 · Cited by 22

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein–Protein Interfaces

Singh, N., Chaput, L., & Villoutreix, B. O. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00545 · Cited by 19

A Teach-Discover-Treat Application of ZincPharmer: An Online Interactive Pharmacophore Modeling and Virtual Screening Tool

Koes, D. R., Pabon, N. A., Deng, X., Phillips, M. A., & Camacho, C. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0134697 · Cited by 20

Knowledge-Based Virtual Screening: Application to the MDM4/p53 Protein–Protein Interaction

Jacoby, E., Boettcher, A., Mayr, L. M., Brown, N., Jenkins, J. L., Kallen, J. A., Engeloch, C., Schopfer, U., Furet, P., Masuya, K., et al. (2009) · Methods in molecular biology · doi:10.1007/978-1-60761-274-2_7 · Cited by 20

Diversity Space and Its Application to Library Selection and Design

Fitzgerald, S. H., Sabat, M., & Geysen, H. M. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci060066z · Cited by 20

Virtual Screening-Based Drug Development for the Treatment of Nervous System Diseases

Li, Q., Ma, Z.-B., Qin, S., & Zhao, W. (2022) · Current Neuropharmacology · doi:10.2174/1570159x20666220830105350 · Cited by 19

Computational chemistry approaches to drug discovery in signal transduction

Fischer, P. M. (2008) · Biotechnology Journal · doi:10.1002/biot.200700259 · Cited by 18

Elaborate ligand-based modeling reveal new migration inhibitory factor inhibitors

Al-Sha’er, M. A., VanPatten, S., Al‐Abed, Y., & Taha, M. O. (2013) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2013.03.003 · Cited by 19

Discovery of new renin inhibitory leads via sequential pharmacophore modeling, QSAR analysis, in silico screening and in vitro evaluation

Al-Nadaf, A. H., & Taha, M. O. (2011) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2011.02.001 · Cited by 19

Novel Cyclooxygenase-1 Inhibitors Discovered Using Affinity Fingerprints

Hsu, N., Cai, D., Damodaran, K., Gomez, R. F., Keck, J., Laborde, E., Lum, R. T., Macke, T. J., Martin, G. R., Schow, S. R., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm049950b · Cited by 19

Hit Expansion Approaches Using Multiple Similarity Methods and Virtualized Query Structures

Bergner, A., & Parel, S. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400059p · Cited by 18

Efficient calculation of compound similarity based on maximum common subgraphs and its application to prediction of gene transcript levels

van Berlo, R. J. P., Winterbach, W., de Groot, M. J. L., Bender, A., Verheijen, P. J. T., Reinders, M. J. T., & de Ridder, D. (2013) · International Journal of Bioinformatics Research and Applications · doi:10.1504/ijbra.2013.054688 · Cited by 18

Modeling Approaches for Ligand-Based 3D Similarity

Tresadern, G., & Bemporad, D. (2010) · Future Medicinal Chemistry · doi:10.4155/fmc.10.244 · Cited by 18

Exploiting personalized information for reagent selection in drug design

Boström, J., Falk, N., & Tyrchan, C. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.01.006 · Cited by 19

Applied Virtual Screening: Strategies, Recommendations, and Caveats

Stumpfe, D., & Bajorath, J. (2011) · Methods and principles in medicinal chemistry · doi:10.1002/9783527633326.ch11 · Cited by 19

Virtual Affinity Fingerprints for Target Fishing: A New Application of Drug Profile Matching

Peragovics, Á., Simon, Z., Tombor, L., Jelinek, B., Hári, P., Czobor, P., & Málnási‐Csizmadia, A. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3004489 · Cited by 17

Ligand-based modelling followed by synthetic exploration unveil novel glycogen phosphorylase inhibitory leads

Habash, M., & Taha, M. O. (2011) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2011.06.086 · Cited by 17

Molecular docking and 4D-QSAR studies of metastatic cancer inhibitor thiazoles

Türkmenoğlu, B., & Güzel, Y. (2018) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2018.07.003 · Cited by 17

Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening

Garcia-Hernandez, C., Fernández, A., & Serratosa, F. (2020) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026620666200603122000 · Cited by 17

Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes

Wills, T. J., Polshakov, D. A., Robinson, M. C., & Lee, A. A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00193 · Cited by 15

Novel Application of 2D and 3D-Similarity Searches To Identify Substrates among Cytochrome P450 2C9, 2D6, and 3A4

de Freitas, R. F., Bauab, R. L., & Montanari, C. A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900074t · Cited by 17

Advances in 2D fingerprint similarity searching

Geppert, H., & Bajorath, J. (2010) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2010.486830 · Cited by 17

Design of a grid service-based platform for in silico protein–ligand screenings

Levesque, M. J., Ichikawa, K., Date, S., & Haga, J. (2008) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2008.07.005 · Cited by 17

Bell inequalities in cardinality-based similarity measurement

Janssens, S. (2006) · Ghent University Academic Bibliography (Ghent University) · Cited by 17

Virtual Drug Screen Schema Based on Multiview Similarity Integration and Ranking Aggregation

Kang, H., Sheng, Z., Zhu, R., Huang, Q., Liu, Q., & Cao, Z. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200481c · Cited by 16

Ligand-based pharmacophore exploration and QSAR analysis of transition state analogues followed by in silico screening guide the discovery of new sub-micromolar β-secreatase inhibitors

Al-Nadaf, A. H., & Taha, M. O. (2012) · Medicinal Chemistry Research · doi:10.1007/s00044-012-0204-x · Cited by 16

Classification of Ligand Molecules in PDB with Fast Heuristic Graph Match Algorithm COMPLIG

Saito, M., Takemura, N., & Shirai, T. (2012) · Journal of Molecular Biology · doi:10.1016/j.jmb.2012.10.001 · Cited by 16

Identification of Nonpeptide Oxytocin Receptor Ligands by Receptor‐Ligand Fingerprint Similarity Search

Weill, N., Valencia, C., Gioria, S., Villa, P., Hibert, M., & Rognan, D. (2011) · Molecular Informatics · doi:10.1002/minf.201100026 · Cited by 19

Discovery of new β‐ D ‐galactosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening

Abdula, A. M., Khalaf, R. A., Mubarak, M. S., & Taha, M. O. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21635 · Cited by 16

Virtual Screening in the Search of New and Potent Anti-Alzheimer Agents

Basile, L. (2017) · Neuromethods · doi:10.1007/978-1-4939-7404-7_4 · Cited by 16

Chemoinformatics approaches to assess chemical diversity and complexity of small molecules

Saldívar‐González, F. I., & Medina‐Franco, J. L. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-818349-6.00003-0 · Cited by 16

Informatics, Machine Learning and Computational Medicinal Chemistry

Mitchell, J. B. O. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.11 · Cited by 16

A visual approach for analysis and inference of molecular activity spaces

Kausar, S., & Falcão, A. O. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0386-z · Cited by 14

Unconventional 2D Shape Similarity Method Affords Comparable Enrichment as a 3D Shape Method in Virtual Screening Experiments

Ebalunode, J. O., & Zheng, W. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900015b · Cited by 15

Lead Discovery and Lead Modification

Silverman, R. B., & Holladay, M. W. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-382030-3.00002-7 · Cited by 17

Discovery of new β-d-glucosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening

Khalaf, R. A., Abdula, A. M., Mubarak, M. S., & Taha, M. O. (2010) · Journal of Molecular Modeling · doi:10.1007/s00894-010-0737-1 · Cited by 15

Local neighborhood behavior in a combinatorial library context

Horvath, D., Koch, C. P., Schneider, G., Marcou, G., & Varnek, A. A. (2011) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9416-2 · Cited by 16

Artificial neural network‐based quantitative structure–activity relationships model and molecular docking for virtual screening of novel potent acetylcholinesterase inhibitors

Zerroug, E., Belaıdı, S., & Chtita, S. (2021) · Journal of the Chinese Chemical Society · doi:10.1002/jccs.202000457 · Cited by 15

An Investigation into the Antifungal Property of Fabaceae using Bioinformatics Tools.

Arabi, Z., & Sardari, S. (2010) · Europe PMC (PubMed Central) · Cited by 15

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein–Protein Interfaces

Singh, N., & Villoutreix, B. O. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232214364 · Cited by 15

Combining Pharmacophore Fingerprints and PLS-Discriminant Analysis for Virtual Screening and SAR Elucidation

Askjær, S., & Langgård, M. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700356w · Cited by 14

Evaluation of a Bayesian inference network for ligand-based virtual screening

Chen, B., Mueller, C., & Willett, P. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-5 · Cited by 14

Predicting the Performance of Fingerprint Similarity Searching

Vogt, M., & Bajorath, J. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_6 · Cited by 14

Molecular Similarity Analysis in Virtual Screening

Peltason, L., & Bajorath, J. (2008) · Royal Society of Chemistry eBooks · doi:10.1039/9781847558879-00120 · Cited by 13

Application of computational methods for class A GPCR Ligand discovery

Szwabowski, G. L., Baker, D. L., & Parrill, A. L. (2023) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2023.108434 · Cited by 12

Identification of lead inhibitors for 3CLpro of SARS-CoV-2 target using machine learning based virtual screening, ADMET analysis, molecular docking and molecular dynamics simulations

Chhetri, S. P., Bhandari, V. S., Maharjan, R., & Lamichhane, T. R. (2024) · RSC Advances · doi:10.1039/d4ra04502e · Cited by 13

Complementing ultrafast shape recognition with an optical isomerism descriptor

Zhou, T., Lafleur, K., & Caflisch, A. (2010) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2010.08.007 · Cited by 13

Increasing the performance, trustworthiness and practical value of machine learning models: a case study predicting hydrogen bond network dimensionalities from molecular diagrams

Frade, A. P., McCabe, P., & Cooper, R. I. (2020) · CrystEngComm · doi:10.1039/d0ce00111b · Cited by 13

Introduction to Molecular Similarity and Chemical Space

Maggiora, G. M. (2014) · Springer eBooks · doi:10.1007/978-3-319-10226-9_1 · Cited by 16

GeneComps and ChemComps: a new CTD metric to identify genes and chemicals with shared toxicogenomic profiles

Davis, A. P., Murphy, C. G., Saraceni-Richards, C. A., Rosenstein, M. C., Wiegers, T. C., Hampton, T. H., & Mattingly, C. J. (2009) · Bioinformation · doi:10.6026/97320630004173 · Cited by 13

Virtual Screening

Good, A. C. (2007) · Elsevier eBooks · doi:10.1016/b0-08-045044-x/00262-5 · Cited by 13

Noncontiguous Atom Matching Structural Similarity Function

Teixeira, A. L., & Falcão, A. O. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400324u · Cited by 13

SwiFT: An Index Structure for Reduced Graph Descriptors in Virtual Screening and Clustering

Fischer, J., & Rarey, M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700007b · Cited by 13

Chemical space and molecular descriptors for QSAR studies

Consonni, V., Ballabio, D., & Todeschini, R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-18638-7.00022-0 · Cited by 13

Online chemical symbol recognition for handwritten chemical expression recognition

Tang, P., Hui, S. C., & Fu, C. (2013) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis.2013.6607894 · Cited by 12

On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?

Guha, R., & Medina‐Franco, J. L. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-11 · Cited by 12

Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

Druzhilovskiy, D. S., Stolbov, L. A., Савосина, П. И., Pogodin, P. V., Filimonov, D. A., Veselovsky, A. V., Stefanisko, K. M., Tarasova, N. I., Nicklaus, M. C., & Poroikov, V. (2020) · Supercomputing Frontiers and Innovations · doi:10.14529/jsfi200306 · Cited by 13

A lattice-based approach for chemical structural retrieval

Tang, P., Hui, S. C., & Fong, A. C. M. (2015) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.12.006 · Cited by 12

Ligand-based designing, in silico screening, and biological evaluation of new potent fructose-1,6-bisphosphatase (FBPase) inhibitors

Tayyem, R. F., Zalloum, H. M., El‐Maghrabi, M. R., Yousef, A., & Mubarak, M. S. (2012) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2012.08.004 · Cited by 12

STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks

Zou, J., Yu, J., Hu, P., Zhao, L., & Shi, S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107691 · Cited by 11

Discovery of novel potent nuclear factor kappa‐B inhibitors (IKK‐β) via extensive ligand‐based modeling and virtual screening

Al-Sha’er, M. A., Almazari, I., & Taha, M. O. (2016) · Journal of Molecular Recognition · doi:10.1002/jmr.2604 · Cited by 11

RED: A Set of Molecular Descriptors Based on Rényi Entropy

Delgado-Soler, L., Toral, R., Belenguer, M. S. T., & Rubio, J. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900275w · Cited by 11

Application of Chemoinformatics to High-Throughput Screening

Parker, C. N., & Schreyer, S. K. (2004) · Methods in molecular biology · doi:10.1385/1-59259-802-1:085 · Cited by 11

Review of knowledge for rational design and identification of anti-tubercular compounds

Siddiqi, M. I., & Kumar, A. (2009) · Expert Opinion on Drug Discovery · doi:10.1517/17460440903253876 · Cited by 10

Similarity Search Profiles as a Diagnostic Tool for the Analysis of Virtual Screening Calculations

Xue, L., Godden, J. W., Stahura, F. L., & Bajorath, J. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci040120g · Cited by 10

Predicting the Polypharmacology of Drugs: Identifying New Uses through Chemoinformatics, Structural Informatics, and Molecular Modeling‐Based Approaches

Xie, L., Kinnings, S., Xie, L., & Bourne, P. E. (2012) · Wiley eBooks · doi:10.1002/9781118274408.ch7 · Cited by 10

Unsupervised pharmacophore modeling combined with QSAR analyses revealed novel low micromolar SIRT2 inhibitors

Khanfar, M. A., & Taha, M. O. (2017) · Journal of Molecular Recognition · doi:10.1002/jmr.2623 · Cited by 9

Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria

Riniker, S., Landrum, G. A., Montanari, F., Villalba, S. D., Maier, J., Jansen, J. M., Walters, W. P., & Shelat, A. A. (2017) · F1000Research · doi:10.12688/f1000research.11905.2 · Cited by 9

Ligand-based modeling of Akt3 lead to potent dual Akt1/Akt3 inhibitor

Al-Sha’er, M. A., & Taha, M. O. (2018) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2018.02.001 · Cited by 9

Computational Drug Repurposing Resources and Approaches for Discovering Novel Antifungal Drugs against Candida albicans N-Myristoyl Transferase

Hussain, A., & Verma, C. K. (2021) · Journal of Pure and Applied Microbiology · doi:10.22207/jpam.15.2.49 · Cited by 10

Discovery of new human epidermal growth factor receptor-2 (HER2) inhibitors for potential use as anticancer agents via ligand-based pharmacophore modeling

Zalloum, H. M., Tayyem, R. F., Abu‐Irmaileh, B., Bustanji, Y. K., Zihlif, M., Mohammad, M., Aburjai, T. A., & Mubarak, M. S. (2015) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2015.06.008 · Cited by 9

The covalent docking software landscape: features and applications in drug design

Singh, N., Vayer, P., & Villoutreix, B. O. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf697 · Cited by 8

Eigenvector centrality based algorithm for finding a maximal common connected vertex induced molecular substructure of two chemical graphs

Parisutham, N., & Rethnasamy, N. (2021) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2021.130980 · Cited by 8

Similarity Searching in Databases of Flexible 3D Structures Using Autocorrelation Vectors Derived from Smoothed Bounded Distance Matrices

Rhodes, N., Clark, D. E., & Willett, P. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci0503863 · Cited by 8

Drug-drug relationship based on target information: application to drug target identification

Park, K., & Kim, D. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-s2-s12 · Cited by 8

Computational approaches for the design of protein–protein interaction inhibitors

Yu, W., Guvench, O., & MacKerell, A. D. (2013) · doi:10.4155/ebo.13.141 · Cited by 8

Python for Chemists

Tanemura, K. A., Sierra-Costa, D., & Merz, K. M. (2021) · ACS in focus · doi:10.1021/acsinfocus.7e5030 · Cited by 7

Cheminformatics Approaches to Study Drug Polypharmacology

Naveja, J. J., Saldívar‐González, F. I., Sánchez‐Cruz, N., & Medina‐Franco, J. L. (2018) · Methods in pharmacology and toxicology · doi:10.1007/7653_2018_6 · Cited by 7

Enhancing the Effectiveness of Fingerprint-Based Virtual Screening: Use of Turbo Similarity Searching and of Fragment Frequencies of Occurrence

Arif, S. M., Hert, J., Holliday, J. D., Malim, N. H. A. H., & Willett, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04031-3_35 · Cited by 7

Tunable Machine Vision-Based Strategy for Automated Annotation of Chemical Databases

Park, J., Rosania, G. R., & Saitou, K. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900029v · Cited by 7

Computational Strategies in Cancer Drug Discovery

Mustata, G. I., & Muftuoglu, Y. (2012) · InTech eBooks · doi:10.5772/24177 · Cited by 7

A novel potential therapeutic avenue for autism: Design, synthesis and pharmacophore generation of SSRIs with dual action

Ghoneim, O. M., Ibrahim, D. A., El-Deeb, I. M., Lee, S. H., & Booth, R. G. (2011) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2011.09.046 · Cited by 7

‘tieredScreen’ – Layered Virtual Screening Tool for the Identification of Novel Estrogen Receptor Alpha Modulators

Yang, Y., Carta, G., Peters, M. B., Price, T., O’Boyle, N. M., Knox, A. J. S., Fayne, D., Williams, D. C., Meegan, M. J., & Lloyd, D. G. (2010) · Molecular Informatics · doi:10.1002/minf.201000034 · Cited by 6

CFam: a chemical families database based on iterative selection of functional seeds and seed-directed compound clustering

Zhang, C., Tao, L., Chu, Q., Zhang, P., Chen, S., Zeng, X. L., Feng, X., Chen, Z., Yang, S. Y., & Chen, Y. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1212 · Cited by 6

Symmetric Kv1.5 Blockers Discovered by Focused Screening

Boström, J. (2012) · ACS Medicinal Chemistry Letters · doi:10.1021/ml3001787 · Cited by 6

Predicting the similarity search performance of fingerprints and their combination with molecular property descriptors using probabilistic and information theoretic modeling

Vogt, M., Nisius, B., & Bajorath, J. (2009) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10035 · Cited by 6

Chemoinformatics

Gillet, V. J., & Leach, A. R. (2007) · Elsevier eBooks · doi:10.1016/b0-08-045044-x/00085-7 · Cited by 6

Knowledge‐Based Approaches for Predicting the Sites and Products of Metabolism

Judson, P. N. (2014) · Methods and principles in medicinal chemistry · doi:10.1002/9783527673261.ch12 · Cited by 6

Pharmacophore Mapping: An Important Tool in Modern Drug Design and Discovery

Pathak, D. V., Vyas, A., Sagar, S. R., Bhatt, H., & Patel, P. K. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815179934123010005 · Cited by 5

Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual Screen

Altalib, M. K., & Salim, N. B. (2022) · Biomolecules · doi:10.3390/biom12111719 · Cited by 5

Ligand-based virtual screening using binary kernel discrimination

Chen, B., Harrison, R. F., Hert, J., Mpanhanga, C., Willett, P., & Wilton, D. J. (2005) · Molecular Simulation · doi:10.1080/08927020500134177 · Cited by 5

ToBio: Global Pathway Similarity Search Based on Topological and Biological Features

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