Cited by 215Computational Drug Discovery MethodsMachine Learning in Materials SciencePharmacogenetics and Drug Metabolism

The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.

Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades

Open access

Göller, Andreas H., Kuhnke, Lara, Montanari, Floriane, Bonin, Anne, Schneckener, Sebastian, ter Laak, Antonius, Wichard, Jörg, Lobell, Mario, Hillisch, Alexander·2020·Drug Discovery Today

DOI 10.1016/j.drudis.2020.07.001

Open this paper in CoChat — free account Open PDF

Abstract

Over the past two decades, an in silico absorption, distribution, metabolism, and excretion (ADMET) platform has been created at Bayer Pharma with the goal to generate models for a variety of pharmacokinetic and physicochemical endpoints in early drug discovery. These tools are accessible to all scientists within the company and can be a useful in assisting with the selection and design of novel leads, as well as the process of lead optimization. Here. we discuss the development of machine-learning (ML) approaches with special emphasis on data, descriptors, and algorithms. We show that high company internal data quality and tailored descriptors, as well as a thorough understanding of the experimental endpoints, are essential to the utility of our models. We discuss the recent impact of deep neural networks and show selected application examples.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.drudis.2020.07.001

Cites

65 of 65 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Support-Vector Networks
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3–25. 1
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(96)00423-1 · Cited by 11,484
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,925
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251
On the generalized distance in statistics
Mahalanobis, P. C. (1936) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 6,068
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107

Show all 62 references

Sorted by citations

ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lü, A., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab255 · Cited by 3,023
Artificial intelligence in drug development
Zhang, K., Yang, X., Wang, Y., Yu, Y., Huang, N., Li, G. L., Li, X., Wu, J. C., & Yang, S. (2025) · Nature Medicine · doi:10.1038/s41591-024-03434-4 · Cited by 585
A review on machine learning approaches and trends in drug discovery
Carracedo-Reboredo, P., Liñares-Blanco, J., Rodríguez-Fernández, N., Cedrón, F., Novoa, F. J., Carballal, A., Maojo, V., Pazos, A., & Fernandez-Lozano, C. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.08.011 · Cited by 517
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 · doi:10.1080/17460441.2021.1909567 · Cited by 464
Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429
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 · doi:10.1016/j.drudis.2020.11.037 · Cited by 232
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236
“Recent lessons shared by scientists at AstraZeneca (83), Bayer (84), Merck (85) and GSK (86) highlight the complexities of ADMET property prediction (87).”
BackgroundInfluential
Artificial intelligence and machine learning in drug discovery and development
Patel, V. J., & Shah, M. (2021) · Intelligent Medicine · doi:10.1016/j.imed.2021.10.001 · Cited by 234
Deep Learning in Virtual Screening: Recent Applications and Developments
Kimber, T. B., Chen, Y., & Volkamer, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22094435 · Cited by 215
Small molecules and their impact in drug discovery: A perspective on the occasion of the 125th anniversary of the Bayer Chemical Research Laboratory
Beck, H., Härter, M. W., Haß, B., Schmeck, C., & Bärfacker, L. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.02.015 · Cited by 201
Enhancing preclinical drug discovery with artificial intelligence
Vijayan, R., Kihlberg, J. O., Cross, J. B., & Poongavanam, V. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.11.023 · Cited by 191
MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information
Heyndrickx, W., Mervin, L., Morawietz, T., Sturm, N., Friedrich, L., Zalewski, A., Pentina, A., Humbeck, L., Oldenhof, M., Niwayama, R., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00799 · Cited by 195
Self-citation
Practical guidelines for the use of gradient boosting for molecular property prediction
Boldini, D., Grisoni, F., Kühn, D., Friedrich, L., & Sieber, S. A. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00743-7 · Cited by 202
Toward Machine Learning-Enhanced High-Throughput Experimentation
Eyke, N. S., Koscher, B. A., & Jensen, K. F. (2021) · Trends in Chemistry · doi:10.1016/j.trechm.2020.12.001 · Cited by 156
Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World
Seal, S., Mahale, M., García-Ortegón, M., Joshi, C. K., Hosseini-Gerami, L., Beatson, A., Greenig, M., Shekhar, M., Patra, A., Weis, C., et al. (2025) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5c00033 · Cited by 168
ADMETboost: a web server for accurate ADMET prediction
Tian, H., Ketkar, R., & Tao, P. (2022) · Journal of Molecular Modeling · doi:10.1007/s00894-022-05373-8 · Cited by 134
LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design
Fialková, V., Zhao, J., Papadopoulos, K., Engkvist, O., Bjerrum, E. J., Kogej, T., & Patronov, A. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00469 · Cited by 107
A practical guide to machine-learning scoring for structure-based virtual screening
Tran‐Nguyen, V., Junaid, M., Simeon, S., & Ballester, P. J. (2023) · Nature Protocols · doi:10.1038/s41596-023-00885-w · Cited by 106
Trends in Molecular Properties, Bioavailability, and Permeability across the Bayer Compound Collection: Miniperspective
O’ Donovan, D. H., De Fusco, C., Kuhnke, L., & Reichel, A. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c01577 · Cited by 83
Self-citation
Evolution of Novartis’ Small Molecule Screening Deck Design
Schuffenhauer, A., Schneider, N., Hintermann, S., Auld, D. S., Blank, J., Cotesta, S., Engeloch, C., Fechner, N., Gaul, C., Giovannoni, J., et al. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c01332 · Cited by 85
Trends in oral small-molecule drug discovery and product development based on product launches before and after the Rule of Five
Stegemann, S., Moreton, C., Svanbäck, S., Box, K. J., Motte, G., & Paudel, A. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103344 · Cited by 83
Compound–protein interaction prediction by deep learning: Databases, descriptors and models
Du, B.-X., Qin, Y., Jiang, Y., Xu, Y., Yiu, S., Yu, H., & Shi, J. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.02.023 · Cited by 76
Recent progress on the prospective application of machine learning to structure-based virtual screening
Ghislat, G., Rahman, T., & Ballester, P. J. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.009 · Cited by 65
Discovery solubility measurement and assessment of small molecules with drug development in mind
Barrett, J. A., Yang, W., Skolnik, S. M., Belliveau, L. M., & Patros, K. M. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.01.017 · Cited by 66
The Whole Is Bigger than the Sum of Its Parts: Drug Transport in the Context of Two Membranes with Active Efflux
Rybenkov, V. V., Zgurskaya, H. I., Ganguly, C., Leus, I. V., Zhang, Z., & Moniruzzaman, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01137 · Cited by 65
DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning
Fralish, Z., Chen, A., Skaluba, P., & Reker, D. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00769-x · Cited by 65
Application of machine learning models for property prediction to targeted protein degraders
Peteani, G., Huynh, M. T. D., Gerebtzoff, G., & Rodríguez-Pérez, R. (2024) · Nature Communications · doi:10.1038/s41467-024-49979-3 · Cited by 73
Yes SIR! On the structure–inactivity relationships in drug discovery
López‐López, E., Gortari, E. F., & Medina‐Franco, J. L. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.05.005 · Cited by 56
Artificial Intelligence for Autonomous Molecular Design: A Perspective
Joshi, R. P., & Kumar, N. (2021) · Molecules · doi:10.3390/molecules26226761 · Cited by 55
Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches
Gorgulla, C., Jayaraj, A., Fackeldey, K., & Arthanari, H. (2022) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2022.102156 · Cited by 50
Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties
Lascio, E. D., Gerebtzoff, G., & Rodríguez-Pérez, R. (2023) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.2c00962 · Cited by 48
Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development
Jung, W. J., Goo, S., Hwang, T., Lee, H., Kim, Y., Chae, J., Yun, H., & Jung, S. (2024) · Pharmaceuticals · doi:10.3390/ph17030382 · Cited by 52
ADMET Predictability at Boehringer Ingelheim: State‐of‐the‐Art, and Do Bigger Datasets or Algorithms Make a Difference?
Aleksić, S., Seeliger, D., & Brown, J. B. (2021) · Molecular Informatics · doi:10.1002/minf.202100113 · Cited by 45
Discovery of IRAK4 Inhibitors BAY1834845 (Zabedosertib) and BAY1830839
Bothe, U., Günther, J., Nubbemeyer, R., Siebeneicher, H., Ring, S., Bömer, U., Peters, M., Rausch, A., Denner, K., Himmel, H. M., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c01714 · Cited by 45
Driving success in personalized medicine through AI-enabled computational modeling
Chakravarty, K., Antontsev, V. G., Bundey, Y., & Varshney, J. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.02.007 · Cited by 43
Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
Thomas, M. C., Boardman, A., García-Ortegón, M., Yang, H., de Graaf, C., & Bender, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_1 · Cited by 43
LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP
Wang, Y., Xiong, J., Xiao, F., Zhang, W., Cheng, K., Rao, J., Niu, B., Tong, X., Qu, N., Zhang, R., et al. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00754-4 · Cited by 45
Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage
Stoyanova, R., Katzberger, P., Komissarov, L., Khadhraoui, A., Sach-Peltason, L., Zbinden, K. G., Schindler, T., & Manevski, N. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01134 · Cited by 39
Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs
Dorahy, G., Chen, Z., & Balle, T. (2023) · Molecules · doi:10.3390/molecules28031324 · Cited by 44
Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade
Serafim, M. S. M., dos Santos, V. S., Gertrudes, J. C., Maltarollo, V. G., & Honório, K. M. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1918098 · Cited by 38
PyPEF—An Integrated Framework for Data-Driven Protein Engineering
Siedhoff, N. E., Illig, A.-M., Schwaneberg, U., & Davari, M. D. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00099 · Cited by 36
Prediction of Human Pharmacokinetics From Chemical Structure: Combining Mechanistic Modeling with Machine Learning
Grüber, A., Führer, F., Menz, S., Diedam, H., Göller, A. H., & Schneckener, S. (2023) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2023.10.035 · Cited by 35
Self-citation
Multispecies Machine Learning Predictions of In Vitro Intrinsic Clearance with Uncertainty Quantification Analyses
Rodríguez-Pérez, R., Trunzer, M., Schneider, N., Faller, B., & Gerebtzoff, G. (2022) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.2c00680 · Cited by 38
Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge
Lenselink, E. B., & Stouten, P. F. W. (2021) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-021-00405-6 · Cited by 39
Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans
Geci, R., Gadaleta, D., de Lomana, M. G., Ortega-Vallbona, R., Colombo, E., Serrano‐Candelas, E., Paini, A., Kuepfer, L., & Schaller, S. (2024) · Archives of Toxicology · doi:10.1007/s00204-024-03764-9 · Cited by 35
Exploring Deep Learning of Quantum Chemical Properties for Absorption, Distribution, Metabolism, and Excretion Predictions
Lim, M. A., Song, Y., Mai, H., & Cheng, A. C. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00245 · Cited by 33
A data science roadmap for open science organizations engaged in early-stage drug discovery
Edfeldt, K., Edwards, A. M., Engkvist, O., Günther, J., Hartley, M., Hulcoop, D. G., Leach, A. R., Marsden, B. D., Menge, A., Misquitta, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49777-x · Cited by 33
A Brief Review of Machine Learning-Based Bioactive Compound Research
Park, J., Beck, B. R., Kim, H. H., Lee, S.-B., & Kang, K. (2022) · Applied Sciences · doi:10.3390/app12062906 · Cited by 30
Modeling and Simulation of Process Technology for Nanoparticulate Drug Formulations—A Particle Technology Perspective
Uhlemann, J., Diedam, H., Hoheisel, W., Schikarski, T., & Peukert, W. (2020) · Pharmaceutics · doi:10.3390/pharmaceutics13010022 · Cited by 29
In silico ADME/tox comes of age: twenty years later
Ekins, S., Lane, T. R., Urbina, F., & Puhl, A. C. (2023) · Xenobiotica · doi:10.1080/00498254.2023.2245049 · Cited by 30
Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule
Sheridan, R. P., Culberson, J. C., Joshi, E., Tudor, M., & Karnachi, P. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00699 · Cited by 28
Cross-Species Extrapolation of Biological Data to Guide the Environmental Safety Assessment of Pharmaceuticals—The State of the Art and Future Priorities
Margiotta‐Casaluci, L., Owen, S. F., & Winter, M. J. (2023) · Environmental Toxicology and Chemistry · doi:10.1002/etc.5634 · Cited by 24
Artificial intelligence drives the digital transformation of pharma
Harrer, S., Menard, J., Rivers, M., Green, D. V. S., Karpiak, J. D., Jeliazkov, J. R., Shapovalov, M. V., del Alamo, D., & Sternke, M. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00049-8 · Cited by 26
A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat
Führer, F., Grüber, A., Diedam, H., Göller, A. H., Menz, S., & Schneckener, S. (2024) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-023-00547-9 · Cited by 24
Self-citation
Conformalized Graph Learning for Molecular ADMET Property Prediction and Reliable Uncertainty Quantification
Li, P., Hua, L., Ma, Z., Hu, W., Liu, Y., & Zhu, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01139 · Cited by 27
Uncertainty Quantification in Molecular Machine Learning for Property Predictions under Data Shifts
Parrondo-Pizarro, R., Lanini, J., & Rodríguez-Pérez, R. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02381 · Cited by 31
Development and Implementation of an enterprise-wide Predictive Model for Early absorption, distribution, Metabolism and Excretion Properties
Kumar., K., V, Chupakhin, V., Vos, A. M., Morrison, D., Rassokhin, D. N., Dellwo, M. J., McCormick, K., Paternoster, E., Ceulemans, H., & DesJarlais, R. L. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2021-0138 · Cited by 25
Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence
Nandi, S., Bhaduri, S., Das, D., Ghosh, P., Mandal, M., & Mitra, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01161 · Cited by 28
A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development
Ali, S., Tian, X., Chen, H., & Zhou, J. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c03159 · Cited by 26
Discovery of BAY-405: An Azaindole-Based MAP4K1 Inhibitor for the Enhancement of T-Cell Immunity against Cancer
Mowat, J., Carretero, R., Leder, G., Font, N. A., Neuhaus, R., Berndt, S., Günther, J., Friberg, A., Schäfer, M., Briem, H., et al. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01325 · Cited by 23
Comprehensive benchmarking of computational tools for predicting toxicokinetic and physicochemical properties of chemicals
Gadaleta, D., Serrano‐Candelas, E., Ortega-Vallbona, R., Colombo, E., de Lomana, M. G., Biava, G., Aparicio-Sánchez, P., Roncaglioni, A., Gozalbes, R., & Benfenati, E. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00931-z · Cited by 22
DeepCt: Predicting Pharmacokinetic Concentration–Time Curves and Compartmental Models from Chemical Structure Using Deep Learning
Beckers, M., Yonchev, D., Desrayaud, S., Gerebtzoff, G., & Rodríguez-Pérez, R. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c00562 · Cited by 21
Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction
Lanevskij, K., Didžiapetris, R., & Sazonovas, A. (2022) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-022-00483-0 · Cited by 20
Deep learning and artificial intelligence for drug discovery, application, challenge, and future perspectives
Ali, N. S., Hanif, N., Khan, H. A., Waseem, M., Saeed, A., Zakir, S., Khan, A., Aamir, M., Ali, A., Ali, A., et al. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06991-6 · Cited by 26
Comparison of new secondgeneration H1 receptor blockers with some molecules; a study involving DFT, molecular docking, ADMET, biological target and activity
Ünsal, V., Öner, E., Yıldız, R., & Mert, B. D. (2025) · BMC Chemistry · doi:10.1186/s13065-024-01371-4 · Cited by 23
Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review
Qian, L., Lu, X., Haris, P. I., Zhu, J., Li, S., & Yang, Y. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104332 · Cited by 23
Use of Free Energy Methods in the Drug Discovery Industry
Meier, K., Bluck, J. P., & Christ, C. D. (2021) · ACS symposium series · doi:10.1021/bk-2021-1397.ch002 · Cited by 20
Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking
Fernandes, P. O., Martins, D. M., Bozzi, A. D. S., Martins, J. P. A., Moraes, A. H., & Maltarollo, V. G. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10261-z · Cited by 19
Bioinformatics and Cheminformatics Tools in Early Drug Discovery
Parikh, P. K., Savjani, J., Gajjar, A. K., & Chhabria, M. T. (2023) · Wiley eBooks · doi:10.1002/9781119865728.ch8 · Cited by 18
Fine-tuning BERT for automatic ADME semantic labeling in FDA drug labeling to enhance product-specific guidance assessment
Shi, Y., Wang, J., Ren, P., ValizadehAslani, T., Zhang, Y., Hu, M., & Liang, H. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104285 · Cited by 18
Novel Mn(II) and Cu(II) metal complexes with sulfa drug-derived ligands as potent antimicrobial and anticancer agents: In vitro studies, ADMET profile and molecular docking
Bourouai, M. A., Bouchoucha, A., Larbi, K. S., Cosnier, S., & Djebbar, S. (2024) · Polyhedron · doi:10.1016/j.poly.2024.116914 · Cited by 19
From NMR to AI: Designing a Novel Chemical Representation to Enhance Machine Learning Predictions of Physicochemical Properties
Leniak, A., Pietruś, W., & Kurczab, R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c02039 · Cited by 18
Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern
Veríssimo, G. C., Serafim, M. S. M., Kronenberger, T., Ferreira, R. S., Honório, K. M., & Maltarollo, V. G. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2114451 · Cited by 17
Lipophilicity and ADMET Analysis of Quinoline-1,4-quinone Hybrids
Kadela-Tomanek, M., Jastrzebska, M. M., Chrobak, E., & Bębenek, E. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics15010034 · Cited by 16
A Combination of In Silico ADMET Prediction, In Vivo Toxicity Evaluation, and Potential Mechanism Exploration of Brucine and Brucine N-oxide—A Comparative Study
Gao, Y., Guo, L., Han, Y., Zhang, J., Dai, Z., & Ma, S. (2023) · Molecules · doi:10.3390/molecules28031341 · Cited by 16
Multi‐Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets**
Walter, M., Borghardt, J. M., Humbeck, L., & Škalič, M. (2024) · Molecular Informatics · doi:10.1002/minf.202400079 · Cited by 18
Artificial intelligence in drug discovery — what it is, where we stand and the path forward
Bender, A., Thomas, M. C., Scannell, J. W., Shaywitz, D. A., Ghiandoni, G. M., Greener, J. G., Pruteanu, L.-L., Jacobson, R. D., Handa, K., Hirano, M., et al. (2026) · Nature Reviews Drug Discovery · doi:10.1038/s41573-026-01496-2 · Cited by 23
Recent Studies of Artificial Intelligence on In Silico Drug Absorption
Tran, T. T. V., Tayara, H., & Chong, K. T. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00960 · Cited by 15
Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects
Fluetsch, A., Lascio, E. D., Gerebtzoff, G., & Rodríguez-Pérez, R. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01124 · Cited by 15
Combining DELs and machine learning for toxicology prediction
Blay, V., Li, X., Gerlach, J., Urbina, F., & Ekins, S. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103351 · Cited by 14
Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study
Rich, A. S., Chan, Y. H., Birnbaum, B., Haider, K., Haimson, J., Hale, M. R., Han, Y., Hickman, W., Hoeflich, K. P., Ortwine, D. F., et al. (2024) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.4c00290 · Cited by 17
Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods
Sauer, S., Matter, H., Heßler, G., & Grebner, C. (2022) · Frontiers in Chemistry · doi:10.3389/fchem.2022.1012507 · Cited by 14
Enhanced utility of AI/ML methods during lead optimization by inclusion of 3D ligand information
Bleicher, L. S., van Daelen, T., Honeycutt, J. D., Hassan, M., Chandrasekhar, J., Shirley, W. A., Tsui, V., & Schmitz, U. (2022) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2022.1074797 · Cited by 14
Free Energy Perturbation Approach for Accurate Crystalline Aqueous Solubility Predictions
Hong, R. S., Rojas, A. V., Bhardwaj, R. M., Wang, L., Mattei, A., Abraham, N. S., Cusack, K. P., Pierce, M. O., Mondal, S., Mehio, N., et al. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c01339 · Cited by 13
Stability of Prediction in Production ADMET Models as a Function of Version: Why and When Predictions Change
Sheridan, R. P. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00803 · Cited by 13
From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output
Baltrukevich, H., & Podlewska, S. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.844293 · Cited by 12
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
Multitask Deep Learning Models of Combined Industrial Absorption, Distribution, Metabolism, and Excretion Datasets to Improve Generalization
Napoli, J. A., Reutlinger, M., Brandl, P., Wang, W., Hert, J., & Desai, P. V. (2025) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.4c01086 · Cited by 12
A Comprehensive Review on Potential In Silico Screened Herbal Bioactive Compounds and Host Targets in the Cardiovascular Disease Therapy
Zarenezhad, E., Hadi, A. T., Nournia, E., Rostamnia, S., & Ghasemian, A. (2024) · BioMed Research International · doi:10.1155/2024/2023620 · Cited by 11
MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database
Mazzolari, A., Scaccabarozzi, A., Vistoli, G., & Pedretti, A. (2021) · Molecules · doi:10.3390/molecules26195857 · Cited by 11
Computational Strategies to Identify New Drug Candidates against Neuroinflammation
Pavan, M., Bassani, D., Bolcato, G., Bissaro, M., Sturlese, M., & Moro, S. (2022) · Current Medicinal Chemistry · doi:10.2174/0929867329666220208095122 · Cited by 11
BioPrint Meets the AI Age: Development of Artificial Intelligence-Based ADMET Models for the Drug-Discovery Platform SAFIRE
Biehn, S. E., Goncalves, L. M., Lehmann, J., Marty, J. D., Mueller, C., Ramirez, S. A., Tillier, F., & Sage, C. R. (2024) · Future Medicinal Chemistry · doi:10.4155/fmc-2024-0007 · Cited by 10
Molecular Docking and Pharmacokinetics Analysis of Phytochemicals from Piper caninum as Dengue NS2B-NS3 Protease Inhibitors
Mustafa, N. F., Cheng, K., Razali, S. A., & Nadri, M. H. (2024) · Journal of Tropical Life Science · doi:10.11594/jtls.14.01.14 · Cited by 10
Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review
Gosavi, A. P. V., Nandgude, T. D., Mishra, R. K., & Puri, D. B. (2024) · AAPS PharmSciTech · doi:10.1208/s12249-024-02816-8 · Cited by 10
Best of both worlds: An expansion of the state of the art pKa model with data from three industrial partners
Fraczkiewicz, R., Nguyen, H. Q., Wu, N., Kausch‐Busies, N., Grimbs, S., Sommer, K., ter Laak, A., Günther, J., Wagner, B., & Reutlinger, M. (2024) · Molecular Informatics · doi:10.1002/minf.202400088 · Cited by 10
Human Brain Penetration Prediction Using Scaling Approach from Animal Machine Learning Models
Liu, S., & Kosugi, Y. (2023) · The AAPS Journal · doi:10.1208/s12248-023-00850-1 · Cited by 9
In silico prediction of dermal absorption from non-dietary exposure to plant protection products
Kuster, C. J., Baumann, J., Braun, S. M., Fisher, P., Hewitt, N. J., Beck, M., Weysser, F., Görlitz, L., Salminen, P., Dietrich, C. R., et al. (2022) · Computational Toxicology · doi:10.1016/j.comtox.2022.100242 · Cited by 9
Computational approaches to DMPK: A realistic assessment of current methods and their practical impact. Part I: Physicochemical and in vitro properties
D, K. H. M., Hirano, M., Kageyama, M., & Bender, A. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104422 · Cited by 8
Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening
Mamada, H., Takahashi, M. I., Ogino, M., Nomura, Y., & Uesawa, Y. (2023) · ACS Omega · doi:10.1021/acsomega.3c04073 · Cited by 8
Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters
Kuroda, M., Watanabe, R., Esaki, T., Kawashima, H., Ohashi, R., Sato, T., Honma, T., Komura, H., & Mizuguchi, K. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.103339 · Cited by 7

Authors: Göller, A. H., Kuhnke, L., Montanari, F., Bonin, A., Schneckener, S., ter Laak, A., Wichard, J., Lobell, M., & Hillisch, A.
Affiliations: Bayer (Germany) (DE)
Corresponding author: Hillisch, Alexander
ORCID: Göller, Andreas H.
Kuhnke, Lara
Montanari, Floriane
Schneckener, Sebastian
Wichard, Jörg
Lobell, Mario
Hillisch, Alexander
Venue: Drug Discovery Today
Publisher: Elsevier BV
Published: 2020-09
Volume: 25
Issue: 9
Pages: 1702-1709
ISSN: 1359-6446 (ISSN-L)
DOI: 10.1016/j.drudis.2020.07.001
OpenAlex: W3041286284
Cited by: 215
References: 62
Citations by year: 2026: 22 · 2025: 38 · 2024: 56 · 2023: 38 · 2022: 30 · 2021: 27 · 2020: 3
FWCI: 2.18
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Drug Discovery Today (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
Keywords: machine learning, pharmacokinetic prediction, physicochemical properties, early drug discovery, deep neural network, molecular descriptors, lead optimization
MeSH terms: Machine Learning, Animals, Computer Simulation, Pharmaceutical Preparations, Humans, Intestinal Absorption, Models, Theoretical, Pharmacokinetics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks