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

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

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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

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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.

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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

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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

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