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References from ADMET all-inclusive: Accountability for the full end-to-end model life cycle is key to success. Local targets link to admitted publications; unresolved targets remain external evidence.
Advances in oral drug delivery: improved bioavailability of poorly absorbed drugs by tissue and cellular optimization
10.1016/j.addr.2012.02.008 · 2012 · External reference
Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection
10.2174/156802605774297038 · 2005 · External reference
Artificial intelligence for drug discovery: are we there yet?
10.1146/annurev-pharmtox-040323-040828 · 2024 · External reference
Can the pharmaceutical industry reduce attrition rates?
10.1038/nrd1470 · 2004 · External reference
Managing the drug discovery/development interface
10.1016/s1359-6446(97)01099-4 · 1997 · External reference
eCounterscreening: using QSAR predictions to prioritize testing for off-target activities and setting the balance between benefit and risk
10.1021/ci500666m · 2015 · External reference
Leveraging machine learning predicted confidence for boosting assay submission and decision-making efficiencies
10.1016/j.ejmech.2025.117947 · 2025 · External reference
Unresolved reference
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Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades
10.1016/j.drudis.2020.07.001 · 2020 · External reference
Machine learning applied to the modeling of pharmacological and ADMET endpoints
2022 · External reference
Toward in silico structure-based ADMET prediction in drug discovery
10.1016/j.drudis.2011.10.023 · 2012 · External reference
Prediction of ADMET properties
10.1002/cmdc.200600155 · 2006 · External reference
ADMET modeling approaches in drug discovery
10.1016/j.drudis.2019.03.015 · 2019 · External reference
The trends and future prospective of in silico models from the viewpoint of ADME evaluation in drug discovery
10.3390/pharmaceutics15112619 · 2023 · External reference
Leveraging machine learning models in evaluating ADMET properties for drug discovery and development
10.5599/admet.2772 · 2026 · External reference
ADMET tools in the digital era: applications and limitations
10.1016/bs.apha.2025.01.004 · 2025 · External reference
ADMET in silico modelling: towards prediction paradise?
10.1038/nrd1032 · 2003 · External reference
Augmenting DMTA using predictive AI modelling at AstraZeneca
10.1016/j.drudis.2024.103945 · 2024 · External reference
Fresh from the biotech pipeline: FDA approvals settle in 2024, but what next?
10.1038/s41587-025-02555-6 · 2025 · External reference
Application of machine learning models for property prediction to targeted protein degraders
10.1038/s41467-024-49979-3 · 2024 · External reference
Serverless prediction of peptide properties with recurrent neural networks
10.1021/acs.jcim.2c01317 · 2023 · External reference
Machine-learning-guided peptide drug discovery: development of GLP-1 receptor agonists with improved drug properties
10.1021/acs.jmedchem.4c00417 · 2024 · External reference
Quantitative structure-activity relationship study of antioxidative peptide by using different sets of amino acid descriptors
10.1016/j.molstruc.2011.05.011 · 2011 · External reference
AZOrange-High performance open source machine learning for QSAR modeling in a graphical programming environment
10.1186/1758-2946-3-28 · 2011 · External reference
QSAR workbench: automating QSAR modeling to drive compound design
10.1007/s10822-013-9648-4 · 2013 · External reference
Employing automated machine learning (AutoML) methods to facilitate the in silico ADMET properties prediction
10.1021/acs.jcim.4c02122 · 2025 · External reference
Best practices for QSAR model development, validation, and exploitation
10.1002/minf.201000061 · 2010 · External reference
Modeling physico-chemical ADMET endpoints with multitask graph convolutional networks
10.3390/molecules25010044 · 2019 · External reference
ADMET predictability at Boehringer Ingelheim: state-of-the-art, and do bigger datasets or algorithms make a difference?
10.1002/minf.202100113 · 2022 · External reference
Efficiency of different measures for defining the applicability domain of classification models
10.1186/s13321-017-0230-2 · 2017 · External reference
Machine learning ADME models in practice: four guidelines from a successful lead optimization case study
10.1021/acsmedchemlett.4c00290 · 2024 · External reference
Unresolved reference
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ADMETboost: a web server for accurate ADMET prediction
10.1007/s00894-022-05373-8 · 2022 · External reference
Evaluation of free online ADMET tools for academic or small biotech environments
10.3390/molecules28020776 · 2023 · External reference
ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties
10.1093/nar/gkab255 · 2021 · External reference
admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties
10.1093/nar/gkae298 · 2024 · External reference
Advanced biological and chemical discovery (ABCD): a centralizing discovery knowledge in an inherently decentralized world
10.1021/ci700267w · 2007 · External reference
FOCUS – development of a global communication and modeling platform for applied and computational medicinal chemists
10.1021/ci500598e · 2015 · External reference
Unresolved reference
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Unresolved reference
2022 · External reference
Computer-assisted drug design
10.1021/jm00147a001 · 1985 · External reference
Prediction of oral bioavailability in rats: transferring insights from in vitro correlations to (deep) machine learning models using in silico model outputs and chemical structure parameters
10.1021/acs.jcim.9b00460 · 2019 · External reference
Site of metabolism prediction based on ab initio derived atom representations
10.1002/cmdc.201700097 · 2017 · External reference
MetScore: site of metabolism prediction beyond cytochrome P450 enzymes
10.1002/cmdc.201800309 · 2018 · External reference
MELLODDY: cross-pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information
10.1021/acs.jcim.3c00799 · 2024 · External reference
A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat
10.1007/s10822-023-00547-9 · 2024 · External reference
Prediction of human pharmacokinetics from chemical structure: combining mechanistic modeling with machine learning
2023 · External reference
Quantum chemical calculations of nitrosamine activation and deactivation pathways for carcinogenicity risk assessment
10.3389/fphar.2024.1415266 · 2024 · External reference
Data exploration for target predictions using proprietary and publicly available data sets
10.1021/acs.chemrestox.4c00347 · 2025 · External reference
Rendezvous in chemical space? Comparing the small molecule compound libraries of Bayer and Schering
10.1016/j.drudis.2011.04.005 · 2011 · External reference
Big Pharma screening collections: more of the same or unique libraries? The Astrazeneca-Bayer Pharma AG case
10.1016/j.drudis.2012.10.011 · 2013 · External reference
Best of both worlds: combining pharma data and state of the art modeling technology to improve in silico pKa prediction
10.1021/ci500585w · 2015 · External reference
Best of both worlds: an expansion of the state of the art pKa model with data from three industrial partners
10.1002/minf.202400088 · 2024 · External reference
Data-driven federated learning in drug discovery with knowledge distillation
10.1038/s42256-025-00991-2 · 2025 · External reference
Unresolved reference
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In silico prediction of buffer solubility based on quantum-mechanical and HQSAR- and topology-based descriptors
10.1021/ci0503210 · 2006 · External reference
CypScore: quantitative prediction of reactivity toward cytochromes P450 based on semiempirical molecular orbital theory
10.1002/cmdc.200800384 · 2009 · External reference
Gaussian process regression models for the prediction of hydrogen bond acceptor strengths
10.1002/minf.201800115 · 2019 · External reference
Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies
10.1186/s13321-019-0381-4 · 2019 · External reference
Mechanistic reactivity descriptors for the prediction of Ames mutagenicity of primary aromatic amines
10.1021/acs.jcim.8b00758 · 2019 · External reference
Predicting DNA-reactivity of N-nitrosamines: a quantum chemical approach
10.1021/acs.chemrestox.2c00217 · 2022 · External reference
When machine learning models learn chemistry I: quantifying explainability with matched molecular pairs
10.1039/d5dd00398a · 2026 · External reference
When machine learning models learn chemistry II: applying WISP to real-world examples
10.1039/d5dd00399g · 2026 · External reference
Accelerating drug discovery through agentic AI: a multi-agent approach to laboratory automation in the DMTA cycle
2025 · External reference
El Agente: an autonomous agent for quantum chemistry
10.1016/j.matt.2025.102263 · 2025 · External reference
