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Insights into the impact of N- and O-methylation on aqueous solubility and lipophilicity using matched molecular pair analysis

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The Fragment Network: A Chemistry Recommendation Engine Built Using a Graph Database

Hall, R. J., Murray, C. W., & Verdonk, M. L. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00809 · Cited by 43 · see citing works

A matched molecular pair (MMP) approach for selecting analogs suitable for structure activity relationship (SAR)-based read across

Lester, C., & Yan, G. (2021) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2021.104966 · Cited by 41 · see citing works

Matched Molecular Pair Analysis of Small Molecule Microarray Data Identifies Promiscuity Cliffs and Reveals Molecular Origins of Extreme Compound Promiscuity

Dimova, D., Hu, Y., & Bajorath, J. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm301292a · Cited by 41 · see citing works

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches

Long, T.-Z., Shi, S., Liu, S., Lü, A., Liu, Z., Li, M., Hou, T., & Cao, D. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01088 · Cited by 40 · see citing works

Improved GNNs for Log D 7.4 Prediction by Transferring Knowledge from Low-Fidelity Data

Duan, Y., Fu, L., Zhang, X., Long, T.-Z., Yuan-Hang, H., Liu, Z., Lü, A., Deng, Y., Hsieh, C., Hou, T., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01564 · Cited by 40 · see citing works

MolOpt: A Web Server for Drug Design using Bioisosteric Transformation

Shan, J., & Ji, C. (2019) · Current Computer - Aided Drug Design · doi:10.2174/1573409915666190704093400 · Cited by 40 · see citing works

Composition and Topology of Activity Cliff Clusters Formed by Bioactive Compounds

Stumpfe, D., Dimova, D., & Bajorath, J. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci400728r · Cited by 39 · see citing works

De novo design – hop(p)ing against hope

Schneider, G. (2012) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2012.06.001 · Cited by 39 · see citing works

Advances in Activity Cliff Research

Dimova, D., & Bajorath, J. (2016) · Molecular Informatics · doi:10.1002/minf.201600023 · Cited by 38 · see citing works

Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression

Horvath, D., Marcou, G., Varnek, A. A., Kayastha, S., de León, A. D. L. V., & Bajorath, J. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00359 · Cited by 38 · see citing works

A Turing Test for Molecular Generators

Bush, J. T., Pogány, P., Pickett, S. D., Barker, M., Baxter, A., Campos, S., Cooper, A. W. J., Hirst, D. J., Inglis, G. G. A., Nadin, A. J., et al. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c01148 · Cited by 37 · see citing works

Deep lead optimization enveloped in protein pocket and its application in designing potent and selective ligands targeting LTK protein

Chen, S. Y., Zhang, O., Jiang, C., Zhao, H., Zhang, X., Chen, M., Liu, Y., Su, Q., Wu, Z., Wang, X., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-00997-w · Cited by 37 · see citing works

A Data-Driven Perspective on Bioisostere Evaluation: Mapping the Benzene Bioisostere Landscape with BioSTAR

Hernández‐Lladó, P., Meanwell, N. A., & Russell, A. J. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c01641 · Cited by 37 · see citing works

Predicting liver cytosol stability of small molecules

Shah, P., Siramshetty, V. B., Zakharov, A. V., Southall, N. T., Xu, X., & Nguyễn, Ð.-T. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00426-7 · Cited by 37 · see citing works

ACGCN: Graph Convolutional Networks for Activity Cliff Prediction between Matched Molecular Pairs

Park, J., Sung, G., Lee, S., Kang, S., & Park, C. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00327 · Cited by 37 · see citing works

Case study: Matched molecular pairs approach in QSAR modelling

Туичиевна, А. Ф., & Ikramov, A. (2017) · International Conference on Information Science and Communications Technologies (ICISCT) · doi:10.1109/icisct.2017.8188583

Advances in Computational Medicinal Chemistry: Matched Molecular Pair Analysis

Wassermann, A. M., Dimova, D., Iyer, P., & Bajorath, J. (2012) · Drug Development Research · doi:10.1002/ddr.21045 · Cited by 30 · see citing works

Matcher: An Open-Source Application for Translating Large Structure/Property Data Sets into Insights for Drug Design

Hoover, A. J., Spale, M., Lahue, B. R., & Bitton, D. A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00015 · Cited by 3 · see citing works

Structure-activity relationship analysis on the basis of matched molecular pairs

Wassermann, A. M. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-s1-o14 · Cited by 2 · see citing works

mmpdb: An Open-Source Matched Molecular Pair Platform for Large Multiproperty Data Sets

Dalke, A., Hert, J., & Krämer, C. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00173 · Cited by 119 · see citing works