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

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ADMET in silico modelling: towards prediction paradise?

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On the Properties of Neural Machine Translation: Encoder-Decoder ApproachesPreprint

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DeepTox: toxicity prediction using deep learning

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Linear regression with censored data

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Chemical space and biology

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Deep learning for computational chemistry

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A graph-convolutional neural network model for the prediction of chemical reactivity

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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations

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Massively Multitask Networks for Drug DiscoveryPreprint

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New Modalities for Challenging Targets in Drug Discovery

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Chemography: The Art of Navigating in Chemical Space

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Toward in silico structure-based ADMET prediction in drug discovery

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Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR

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In Silico ADMET Prediction: Recent Advances, Current Challenges and Future Trends

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Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation

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Efficient multi-objective molecular optimization in a continuous latent space

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In silicoADME-Tox modeling: progress and prospects

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Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination

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In-Silico ADME Models: A General Assessment of their Utility in Drug Discovery Applications

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In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development

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Intramolecular hydrogen bonding: An opportunity for improved design in medicinal chemistry

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Guidance Document on Good In Vitro Method Practices (GIVIMP)

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Improving the hit-to-lead process: data-driven assessment of drug-like and lead-like screening hits

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Using Random Forest To Model the Domain Applicability of Another Random Forest Model

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Healthy skepticism: assessing realistic model performance

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Why Some Targets Benefit from beyond Rule of Five Drugs

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Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks

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In Silico ADMET Traffic Lights as a Tool for the Prioritization of HTS Hits

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Best of Both Worlds: Combining Pharma Data and State of the Art Modeling Technology To Improve in Silico pKa Prediction

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Molecular Dynamics Fingerprints (MDFP): Machine Learning from MD Data To Predict Free-Energy Differences

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Efficiency of different measures for defining the applicability domain of classification models

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CypScore: Quantitative Prediction of Reactivity toward Cytochromes P450 Based on Semiempirical Molecular Orbital Theory

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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

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Conformational Sampling of Macrocyclic Drugs in Different Environments: Can We Find the Relevant Conformations?

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Computational Chemistry in the Pharmaceutical Industry: From Childhood to Adolescence

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Discovery and Characterization of the Potent and Selective P2X4 Inhibitor N -[4-(3-Chlorophenoxy)-3-sulfamoylphenyl]-2-phenylacetamide (BAY-1797) and Structure-Guided Amelioration of Its CYP3A4 Induction Profile

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An approach towards enhancement of a screening library: The Next Generation Library Initiative (NGLI) at Bayer — against all odds?

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Utility of protein structures in overcoming ADMET-related issues of drug-like compounds

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Predictivity of Simulated ADME AutoQSAR Models over Time

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Modeling Industrial ADMET Data with Multitask NetworksPreprint

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MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes

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Robust molecular representations for modelling and design derived from atomic partial charges

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Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies

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Site of Metabolism Prediction Based on ab initio Derived Atom Representations

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Mechanistic Reactivity Descriptors for the Prediction of Ames Mutagenicity of Primary Aromatic Amines

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Reliable and Performant Identification of Low-Energy Conformers in the Gas Phase and Water

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Rendezvous in chemical space? Comparing the small molecule compound libraries of Bayer and Schering

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Identification of a Benzimidazolecarboxylic Acid Derivative (BAY 1316957) as a Potent and Selective Human Prostaglandin E2 Receptor Subtype 4 (hEP4-R) Antagonist for the Treatment of Endometriosis

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Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths

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How to Model Inter- and Intramolecular Hydrogen Bond Strengths with Quantum Chemistry

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Discovery and optimization of pyridyl-cycloalkyl-carboxylic acids as inhibitors of microsomal prostaglandin E synthase-1 for the treatment of endometriosis

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Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

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The art and practice of structure-based drug design: A molecular modeling perspective

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ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties

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Artificial intelligence in drug development

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A review on machine learning approaches and trends in drug discovery

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Artificial intelligence in drug discovery: recent advances and future perspectives

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Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

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

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Artificial Intelligence for Drug Discovery: Are We There Yet?

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Artificial intelligence and machine learning in drug discovery and development

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Deep Learning in Virtual Screening: Recent Applications and Developments

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Small molecules and their impact in drug discovery: A perspective on the occasion of the 125th anniversary of the Bayer Chemical Research Laboratory

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Enhancing preclinical drug discovery with artificial intelligence

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MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information

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Practical guidelines for the use of gradient boosting for molecular property prediction

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Toward Machine Learning-Enhanced High-Throughput Experimentation

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Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

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ADMETboost: a web server for accurate ADMET prediction

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LibINVENT: Reaction-based Generative Scaffold Decoration for in Silico Library Design

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A practical guide to machine-learning scoring for structure-based virtual screening

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Trends in Molecular Properties, Bioavailability, and Permeability across the Bayer Compound Collection: Miniperspective

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Evolution of Novartis’ Small Molecule Screening Deck Design

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Trends in oral small-molecule drug discovery and product development based on product launches before and after the Rule of Five

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Compound–protein interaction prediction by deep learning: Databases, descriptors and models

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Recent progress on the prospective application of machine learning to structure-based virtual screening

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Discovery solubility measurement and assessment of small molecules with drug development in mind

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The Whole Is Bigger than the Sum of Its Parts: Drug Transport in the Context of Two Membranes with Active Efflux

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DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning

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Application of machine learning models for property prediction to targeted protein degraders

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Yes SIR! On the structure–inactivity relationships in drug discovery

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Artificial Intelligence for Autonomous Molecular Design: A Perspective

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Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches

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Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties

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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 51 · see citing works

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 44 · see citing works

Discovery of IRAK4 Inhibitors BAY1834845 (Zabedosertib) and BAY1830839

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Driving success in personalized medicine through AI-enabled computational modeling

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Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges

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LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

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Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage

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Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Dorahy, G., Chen, Z., & Balle, T. (2023) · Molecules · doi:10.3390/molecules28031324 · Cited by 41 · see citing works

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., & Honorio, K. M. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1918098 · Cited by 38 · see citing works

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 35 · see citing works

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 · see citing works

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 35 · see citing works

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 34 · see citing works

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans

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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 31 · see citing works

A data science roadmap for open science organizations engaged in early-stage drug discovery

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A Brief Review of Machine Learning-Based Bioactive Compound Research

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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 27 · see citing works

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 29 · see citing works

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 26 · see citing works

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 · see citing works

Artificial intelligence drives the digital transformation of pharma

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A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat

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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 25 · see citing works

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 23 · see citing works

Development and Implementation of an enterprise-wide Predictive Model for Early absorption, distribution, Metabolism and Excretion Properties

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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 23 · see citing works

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 23 · see citing works

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 22 · see citing works

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 · see citing works

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 · see citing works

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 21 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 19 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 18 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 16 · see citing works

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 22 · see citing works

Recent Studies of Artificial Intelligence on In Silico Drug Absorption

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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 · see citing works

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 · see citing works

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 15 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 12 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Machine Learning for In Silico ADMET Prediction

Jia, L., & Gao, H. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_20 · Cited by 35 · see citing works

In Silico ADMET Profiling: Evolution from Traditional Models to Deep Learning Techniques

Kumar, A., Yadav, M., Ray, P. K., Mishra, R., Upadhyay, A., Tripathi, S. M., & Mishra, S. (2026) · Current Topics in Medicinal Chemistry · doi:10.2174/0115680266439252260329214559 · Cited by 1 · see citing works

In silico ADMT prediction

Mutthuraj, D., Sahana, H. D., Sajana, M., Brunda, M., Tejas, P., & Basalingappa, K. M. (2026) · CRC Press eBooks · doi:10.1201/9781003672760-2

In Silico ADMET Prediction Models

Nowak, S., Rossi, M., & Kovacs, C. (2024) · Biomedical and Pharmacological Literature Archives · doi:10.5281/zenodo.19511827

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Komura, H., Watanabe, R., & Mizuguchi, K. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15112619 · Cited by 106 · see citing works