Schneider, Gisbert

Counting on natural products for drug design

Rodrigues, T., Reker, D., Schneider, P., & Schneider, G. (2016) · Nature Chemistry

Computational Drug Discovery MethodsBioactive natural compounds

Cited by 1,250

Drug discovery with explainable artificial intelligence

Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence

Explainable Artificial Intelligence (XAI)Computational Drug Discovery Methods

Cited by 1,063

Automating drug discovery

Schneider, G. (2018) · Nature Reviews Drug Discovery

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 910

Rethinking drug design in the artificial intelligence era

Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery

Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and Education

Cited by 894

Deep Learning in Drug Discovery

Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 723

Artificial intelligence in drug discovery: recent advances and future perspectives

Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 464

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

Merk, D., Friedrich, L., Grisoni, F., & Schneider, G. (2018) · Molecular Informatics

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 373

Helicobacter pylori HtrA is a new secreted virulence factor that cleaves E‐cadherin to disrupt intercellular adhesion

Hoy, B., Löwer, M., Weydig, C., Carra, G., Tegtmeyer, N., Geppert, T., Schröder, P., Sewald, N., Backert, S., Schneider, G., et al. (2010) · The EMBO Reports

Helicobacter pylori-related gastroenterology studiesProtease and Inhibitor Mechanisms

Cited by 295

Recurrent Neural Network Model for Constructive Peptide Design

Müller, A. T., Hiss, J. A., & Schneider, G. (2018) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsAntimicrobial Peptides and Activities

Cited by 270

DOGS: Reaction-Driven de novo Design of Bioactive Compounds

Hartenfeller, M., Zettl, H., Walter, M., Rupp, M., Reisen, F., Proschak, E., Weggen, S., Stark, H., & Schneider, G. (2012) · PLoS Computational Biology

Computational Drug Discovery MethodsSynthesis and Characterization of Heterocyclic Compounds

Cited by 251

Generative molecular design in low data regimes

Moret, M., Friedrich, L., Grisoni, F., Merk, D., & Schneider, G. (2020) · Nature Machine Intelligence

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 219

modlAMP: Python for antimicrobial peptides

Müller, A. T., Gabernet, G., Hiss, J. A., & Schneider, G. (2017) · Bioinformatics

Antimicrobial Peptides and ActivitiesComputational Drug Discovery Methods

Cited by 215

A critical overview of computational approaches employed for COVID-19 drug discovery

Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., et al. (2021) · Chemical Society Reviews

Computational Drug Discovery MethodsSARS-CoV-2 and COVID-19 Research

Cited by 206

Combining generative artificial intelligence and on-chip synthesis for de novo drug design

Grisoni, F., Huisman, B. J. H., Button, A. L., Moret, M., Atz, K., Merk, D., & Schneider, G. (2021) · Science Advances

Computational Drug Discovery MethodsInnovative Microfluidic and Catalytic Techniques Innovation

Cited by 190

Structure-based drug design with geometric deep learning

Isert, C., Atz, K., & Schneider, G. (2023) · Current Opinion in Structural Biology

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 182

Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment

Jiménez-Luna, J., Skalic, M., Weskamp, N., & Schneider, G. (2021) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsExplainable Artificial Intelligence (XAI)

Cited by 96

Prediction of Hepatic Metabolic Clearance: Comparison and Assessment of Prediction Models

Zuegge, J., Schneider, G., Coassolo, P., & Lav??, T. (2001) · Clinical Pharmacokinetics

Pharmacogenetics and Drug MetabolismPharmaceutical studies and practices

Cited by 92

Context-Based Identification of Protein-Protein Interfaces and “Hot-Spot” Residues

Geppert, T., Hoy, B., Wessler, S., & Schneider, G. (2011) · Chemistry & Biology

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 80

Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery

Reutlinger, M., & Schneider, G. (2012) · Journal of Molecular Graphics and Modelling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 72

Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides

Schneider, P., Müller, A. T., Gabernet, G., Button, A. L., Posselt, G., Wessler, S., Hiss, J. A., & Schneider, G. (2017) · Molecular Informatics

Computational Drug Discovery MethodsAntimicrobial Peptides and Activities

Cited by 52

Identification of UV-protective Activators of Nuclear Factor Erythroid-derived 2-Related Factor 2 (Nrf2) by Combining a Chemical Library Screen with Computer-based Virtual Screening

Lieder, F., Reisen, F., Geppert, T., Sollberger, G., Beer, H.-D., auf dem Keller, U., Schäfer, M., Detmar, M., Schneider, G., & Werner, S. (2012) · Journal of Biological Chemistry

Genomics, phytochemicals, and oxidative stressSkin Protection and Aging

Cited by 27