Bender, Andreas

Recognizing Pitfalls in Virtual Screening: A Critical Review

Scior, T., Bender, A., Tresadern, G., Medina-Franco, J. L., Martínez-Mayorga, K., Langer, T., Cuanalo-Contreras, K., & Agrafiotis, D. K. (2012) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsSynthesis and Characterization of Heterocyclic Compounds

Cited by 438

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet

Bender, A., & Cortés-Ciriano, I. (2021) · Drug Discovery Today

Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and Education

Cited by 405

Integrating high-content screening and ligand-target prediction to identify mechanism of action

Young, D. W., Bender, A., Hoyt, J., McWhinnie, E., Chirn, G.-W., Tao, C. Y., Tallarico, J. A., Labow, M., Jenkins, J. L., Mitchison, T. J., et al. (2008) · Nature Chemical Biology

Computational Drug Discovery MethodsCell Image Analysis Techniques

Cited by 345

Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance

Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S. (2004) · Journal of Chemical Information and Computer Sciences

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 322

Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data

Bender, A., & Cortes-Ciriano, I. (2021) · Drug Discovery Today

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 232

Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

van Westen, G. J. P., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W. T., & Bender, A. (2011) · MedChemComm

Computational Drug Discovery MethodsProtein Interaction Studies and Fluorescence Analysis

Cited by 176

“Bayes Affinity Fingerprints” Improve Retrieval Rates in Virtual Screening and Define Orthogonal Bioactivity Space: When Are Multitarget Drugs a Feasible Concept?

Bender, A., Jenkins, J. L., Glick, M., Deng, Z., Nettles, J. H., & Davies, J. W. (2006) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 110

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

Thomas, M., Smith, R. T., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021) · Journal of Cheminformatics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 78

Discovery of a small-molecule inhibitor of specific serine residue BAD phosphorylation

Pandey, V., Wang, B., Mohan, C. D., Raquib, A. R., Rangappa, S., Srinivasa, V., Fuchs, J. E., Girish, K. S., Zhu, T., Bender, A., et al. (2018) · Proceedings of the National Academy of Sciences

Cell death mechanisms and regulationPI3K/AKT/mTOR signaling in cancer

Cited by 59

Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout

Cortés-Ciriano, I., & Bender, A. (2019) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsAdversarial Robustness in Machine Learning

Cited by 51

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature

Trapotsi, M.-A., Mouchet, E., Williams, G., Monteverde, T., Juhani, K., Turkki, R., Miljković, F., Martinsson, A., Mervin, L., Pryde, K. R., et al. (2022) · ACS Chemical Biology

Protein Degradation and InhibitorsCell Image Analysis Techniques

Cited by 51

Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules

Paricharak, S., Cortés-Ciriano, I., IJzerman, A. P., Malliavin, T. E., & Bender, A. (2015) · Journal of Cheminformatics

Computational Drug Discovery MethodsProtein Interaction Studies and Fluorescence Analysis

Cited by 38

New associations between drug-induced adverse events in animal models and humans reveal novel candidate safety targets

Giblin, K. A., Basili, D., Afzal, A. M., Rosenbrier-Ribeiro, L., Greene, N., Barrett, I., Hughes, S. J., & Bender, A. (2021) · Chemical Research in Toxicology

Animal testing and alternativesComputational Drug Discovery Methods

Cited by 18

Statistical analysis of preclinical inter-species concordance of histopathological findings in the eTOX database

Wright, P. S. R., Briggs, K. A., Thomas, R., Smith, G. F., Maglennon, G., Mikulskis, P., Chapman, M., Greene, N., Phillips, B. U., & Bender, A. (2023) · Regulatory Toxicology and Pharmacology

Animal testing and alternativesImmunotoxicology and immune responses

Cited by 8

Prediction and identification of synergistic compound combinations against pancreatic cancer cells

KalantarMotamedi, Y., Choi, R. J., Koh, S.-B., Bramhall, J. L., Fan, T.-P., & Bender, A. (2021) · iScience

Computational Drug Discovery MethodsPancreatic and Hepatic Oncology Research

Cited by 8

Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models

Handa, K., Sasaki, S., Asano, S., Kageyama, M., Iijima, T., & Bender, A. (2024) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsDrug Transport and Resistance Mechanisms

Cited by 6

Prediction of Inhibitory Activity Against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models

Handa, K., Sasaki, S., Asano, S., Kageyama, M., Iijima, T., & Bender, A. (2024) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsDrug Transport and Resistance Mechanisms

Cited by 6

AI Agents in Drug Discovery

Seal, S., Huynh, D. L., Chelbi, M., Khosravi, S., Kumar, A., Thieme, M., Wilks, I., Davies, M., Mustali, J., Sun, Y., et al. (2025) · arXiv

Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and Education

Cited by 1