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Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No)

Venkatraman, V., Gaiser, J., Demekas, D., Roy, A., Xiong, R., & Wheeler, T. J. (2024) · Pharmaceuticals · doi:10.3390/ph17080992 · Cited by 13

Data-Driven Derivation of an “Informer Compound Set” for Improved Selection of Active Compounds in High-Throughput Screening

Paricharak, S., IJzerman, A. P., Jenkins, J. L., Bender, A., & Nigsch, F. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00244 · Cited by 13

Continuous Flow as Enabling Technology: Synthesis of Heteroaromatic Sulfinates as Bench Stable Cross-Coupling Partners

Lima, F., André, J., Marziale, A. N., Greb, A., Glowienke, S., Meisenbach, M., Schenkel, B., Martin, B. R., & Sedelmeier, J. (2020) · Organic Letters · doi:10.1021/acs.orglett.0c02155 · Cited by 15

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

Simone, G. D., Sardina, D. S., Gulotta, M. R., & Perricone, U. (2022) · Scientific Reports · doi:10.1038/s41598-022-22324-8 · Cited by 13

Foodinformatics: Quantitative Structure‐Property Relationship Modeling of Volatile Organic Compounds in Peppers

Rojas, C., Duchowicz, P. R., & Castro, E. A. (2019) · Journal of Food Science · doi:10.1111/1750-3841.14477 · 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

Artificial Intelligence in ADME Property Prediction

Siramshetty, V. B., Xu, X., & Shah, P. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3441-7_17 · Cited by 13

Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data

Cirino, T., Pinto, L., Iwan, M., Dougha, A., Lučić, B., Kraljević, A., Navoyan, Z. E., Tevosyan, A., Yeghiazaryan, H., Khondkaryan, L., et al. (2025) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5c00018 · Cited by 14

The importance of molecular complexity in the design of screening libraries

Nilar, S., Ma, N. L., & Keller, T. H. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9683-1 · Cited by 12

Antiprotozoal QSAR modelling for trypanosomiasis (Chagas disease) based on thiosemicarbazone and thiazole derivatives

González, D. L. N., Castaño, J. A. G., Núñez, W. E. R., & Duchowicz, P. R. (2020) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2020.107821 · Cited by 12

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

Ferdous, N., Reza, M. N., Hossain, M. U., Mahmud, S., Napis, S., Chowdhury, K., & Mohiuddin, A. K. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287179 · Cited by 14

Asymmetric Clustering Index in a Case Study of 5-HT1A Receptor Ligands

Śmieja, M., Warszycki, D., Tabor, J., & Bojarski, A. J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102069 · Cited by 12

QSAR study and rustic ligand-based virtual screening in a search for aminooxadiazole derivatives as PIM1 inhibitors

Aouidate, A., Ghaleb, A., Ghamali, M., Chtita, S., Ousaa, A., Choukrad, M., Sbai, A., Bouachrıne, M., & Lakhlifi, T. (2018) · Chemistry Central Journal · doi:10.1186/s13065-018-0401-x · Cited by 12

Extremely Randomized Machine Learning Methods for Compound Activity Prediction

Czarnecki, W. M., Podlewska, S., & Bojarski, A. J. (2015) · Molecules · doi:10.3390/molecules201119679 · Cited by 12

Stereoselective Synthesis of Alkylidene Phthalides

Dragan, A., Jones, D. H., Kennedy, A. R., & Tomkinson, N. C. O. (2016) · Organic Letters · doi:10.1021/acs.orglett.6b01203 · Cited by 12

Data and text mining Chemical substructures that enrich for biological activity

Klekota, J., & Roth, F. P. (2016)

Privilegierte Strukturen neu betrachtet

Schneider, P., & Schneider, G. (2017) · Angewandte Chemie · doi:10.1002/ange.201702816 · Cited by 8

Statistical investigation of the data file on biologically active compounds. V. Allowance for the novelty of the chemical structure in the prediction of the biological activity by an improved method of substructural analysis

Avidon, V. V., Arolovich, V. S., Blinova, V. G., & Freidina, A. M. (1983) · Pharmaceutical Chemistry Journal · doi:10.1007/bf00765247 · Cited by 3

Privileged Structures: Applications in Drug Discovery

DeSimone, R., Currie, K., Mitchell, S. A., Darrow, J. W., & Pippin, D. A. (2004) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207043328544 · Cited by 579

Finding Multiactivity Substructures by Mining Databases of Drug-Like Compounds

Sheridan, R. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci030004y · Cited by 34