Predicting With Confidence: Using Conformal Prediction in Drug Discovery

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

Data Mining and Knowledge Discovery Handbook

(2010) · doi:10.1007/978-0-387-09823-4 · Cited by 2,190 · see citing works

Data Mining for Imbalanced Datasets: An Overview

Chawla, N. V. (2009) · Springer eBooks · doi:10.1007/978-0-387-09823-4_45 · Cited by 1,092 · see citing works

On a simple approach for determining applicability domain of QSAR models

Roy, K., Kar, S., & Ambure, P. S. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.04.013 · Cited by 800 · see citing works

Role of the Breast Cancer Resistance Protein (BCRP/ABCG2) in Drug Transport—an Update

Mao, Q., & Unadkat, J. D. (2014) · The AAPS Journal · doi:10.1208/s12248-014-9668-6 · Cited by 676 · see citing works

A tutorial on conformal prediction

Shafer, G., & Vovk, V. (2007) · arXiv · doi:10.48550/arxiv.0706.3188 · Cited by 655 · see citing works

The Role of ABC Transporters in Drug Resistance, Metabolism and Toxicity

Glavinas, H., Krajcsi, P., Cserepes, J., & Sarkadi, B. (2004) · Current Drug Delivery · doi:10.2174/1567201043480036 · Cited by 559 · see citing works

The P-glycoprotein multidrug transporter

Sharom, F. J. (2011) · Essays in Biochemistry · doi:10.1042/bse0500161 · Cited by 508 · see citing works

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

Norinder, U., Carlsson, L., Boyer, S. K., & Eklund, M. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5001168 · Cited by 208 · see citing works

Cross-conformal predictors

Vovk, V. (2013) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-013-9368-4 · Cited by 174 · see citing works

Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications

Balasubramanian, V. N., Ho, S.-S., & Vovk, V. (2014) · Cited by 155 · see citing works

Role of the bile salt export pump, BSEP, in acquired forms of cholestasis

Stieger, B. (2009) · Drug Metabolism Reviews · doi:10.3109/03602530903492004 · Cited by 134 · see citing works

Principles of Clinical Pharmacology

Atkinson, A. J. (2007) · Elsevier eBooks · doi:10.1016/b978-0-12-369417-1.x5039-1 · Cited by 129 · see citing works

Principles of Clinical Pharmacology

Zakusov, V. V. (2012) · Elsevier eBooks · doi:10.1016/c2009-0-63932-5 · Cited by 96 · see citing works

The application of conformal prediction to the drug discovery process

Eklund, M., Norinder, U., Boyer, S. K., & Carlsson, L. (2013) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-013-9378-2 · Cited by 73 · see citing works

Conformal Regression for Quantitative Structure–Activity Relationship Modeling—Quantifying Prediction Uncertainty

Svensson, F., Aniceto, N., Norinder, U., Cortés‐Ciriano, I., Spjuth, O., Carlsson, L., & Bender, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00054 · Cited by 72 · see citing works

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets

Sun, J., Carlsson, L., Ahlberg, E., Norinder, U., Engkvist, O., & Chen, H. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00159 · Cited by 69 · see citing works

Modelling compound cytotoxicity using conformal prediction and PubChem HTS data

Svensson, F. J. E., Norinder, U., & Bender, A. (2016) · Toxicology Research · doi:10.1039/c6tx00252h · Cited by 63 · see citing works

Resampling methods improve the predictive power of modeling in class-imbalanced datasets

Lee, P. (2014) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph110909776 · Cited by 62 · see citing works

Binary classification of imbalanced datasets using conformal prediction

Norinder, U., & Boyer, S. K. (2017) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2017.01.008 · Cited by 62 · see citing works

A confidence predictor for logD using conformal regression and a support-vector machine

Lapins, M., Arvidsson, S., Lampa, S., Berg, A., Schaal, W., Alvarsson, J., & Spjuth, O. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0271-1 · Cited by 58 · see citing works

ATP binding cassette systems: structures, mechanisms, and functions

Licht, A., & Schneider, E. (2011) · Open Life Sciences · doi:10.2478/s11535-011-0054-4 · Cited by 54 · see citing works

Aggregated Conformal Prediction

Carlsson, L., Eklund, M., & Norinder, U. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44722-2_25 · Cited by 50 · see citing works

A New Imbalanced Learning and Dictions Tree Method for Breast Cancer Diagnosis

Parvïn, H., Bidgoli, B. M., & Alinejad‐Rokny, H. (2013) · Journal of Bionanoscience · doi:10.1166/jbns.2013.1162 · Cited by 46 · see citing works

Vienna LiverTox Workspace—A Set of Machine Learning Models for Prediction of Interactions Profiles of Small Molecules With Transporters Relevant for Regulatory Agencies

Montanari, F., Knasmuller, B., Kohlbacher, S. M., Hillisch, C., Baierová, C., Grandits, M., & Ecker, G. F. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00899 · Cited by 35 · see citing works

Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction

Lampa, S., Alvarsson, J., Arvidsson, S., Berg, A., Ahlberg, E., & Spjuth, O. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01256 · Cited by 34 · see citing works

Integrated Decision Support for Assessing Chemical Liabilities

Spjuth, O., Eklund, M., Helgee, E. A., Boyer, S. K., & Carlsson, L. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200242c · Cited by 33 · see citing works

Reliable Confidence Predictions Using Conformal Prediction

Linusson, H., Johansson, U., Boström, H., & Löfström, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31753-3_7 · Cited by 13 · see citing works

Improving peptide-MHC class I binding prediction for unbalanced datasets

Sales, A. P., Tomaras, G. D., & Kepler, T. B. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-385 · Cited by 11 · see citing works

Training conformal predictorsPreprint

Colombo, N., & Vovk, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.07037 · Cited by 3 · see citing works

Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis

Lambert, B., Forbes, F., Doyle, S., Dehaene, H., & Dojat, M. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102830 · Cited by 318 · see citing works

GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning

Esposito, C., Landrum, G. A., Schneider, N., Stiefl, N., & Riniker, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00160 · Cited by 203 · see citing works

Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

Olsson, H., Kartasalo, K., Mulliqi, N., Capuccini, M., Ruusuvuori, P., Samaratunga, H., Delahunt, B., Lindskog, C., Janssen, E. A. M., Blilie, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34945-8 · Cited by 111 · see citing works

Graph Neural Networks in Modern AI-Aided Drug Discovery

Zhang, O., Lin, H., Zhang, X., Wang, X., Wu, Z., Ye, Q., Zhao, W., Wang, J., Ying, K., Kang, Y., et al. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00461 · Cited by 103 · see citing works

Predictive Modeling in Medicine

Toma, M., & Ong, C. W. (2023) · Encyclopedia · doi:10.3390/encyclopedia3020042 · Cited by 70 · see citing works

Assigning confidence to molecular property prediction

Nigam, A., Pollice, R., Hurley, M. F. D., Hickman, R. J., Aldeghi, M., Yoshikawa, N., Chithrananda, S., Voelz, V. A., & Aspuru‐Guzik, A. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1925247 · Cited by 68 · see citing works

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works

A (Comprehensive) Review of the Application of Quantitative Structure–Activity Relationship (QSAR) in the Prediction of New Compounds with Anti-Breast Cancer Activity

Vasilev, B., & Atanasova, M. (2025) · Applied Sciences · doi:10.3390/app15031206 · Cited by 50 · see citing works

Validation guidelines for drug-target prediction methods

Tanoli, Z., Schulman, A., & Aittokallio, T. A. (2024) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2024.2430955 · Cited by 51 · see citing works

Air traffic controller workload level prediction using conformalized dynamical graph learning

Pang, Y., Hu, J., Lieber, C. S., Cooke, N. J., & Liu, Y. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102113 · Cited by 53 · see citing works

TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses

Sun, E., Ma, R., Negredo, P. N., Brunet, A., & Zou, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02184-y · Cited by 39 · see citing works

Impact of Artificial Intelligence on Compound Discovery, Design, and Synthesis

Miljković, F. N., Rodríguez-Pérez, R., & Bajorath, J. (2021) · ACS Omega · doi:10.1021/acsomega.1c05512 · Cited by 39 · see citing works

In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods

Fagerholm, U., Hellberg, S., Alvarsson, J., & Spjuth, O. (2022) · Alternatives to Laboratory Animals · doi:10.1177/02611929221148447 · Cited by 29 · see citing works

Classifying rockburst with confidence: A novel conformal prediction approach

Ibrahim, B., & Ahenkorah, I. (2024) · International Journal of Mining Science and Technology · doi:10.1016/j.ijmst.2023.12.005 · Cited by 28 · see citing works

CPSign: conformal prediction for cheminformatics modeling

Arvidsson, S., Norinder, U., Alvarsson, J., Ahlberg, E., Carlsson, L., & Spjuth, O. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00870-9 · Cited by 28 · see citing works

Evaluation and Optimization Methods for Applicability Domain Methods and Their Hyperparameters, Considering the Prediction Performance of Machine Learning Models

Kaneko, H. (2024) · ACS Omega · doi:10.1021/acsomega.3c08036 · Cited by 27 · see citing works

Wind speed forecasting approach using conformal prediction and feature importance selection

Züge, C. V., Stefenon, S. F., Yamaguchi, C. K., Mariani, V. C., González, G. V., & Coelho, L. D. S. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.110700 · Cited by 27 · see citing works

Chemoinformatic regression methods and their applicability domain

Dutschmann, T., Schlenker, V., & Baumann, K. (2024) · Molecular Informatics · doi:10.1002/minf.202400018 · Cited by 26 · see citing works

In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models

Fagerholm, U., Hellberg, S., Alvarsson, J., Arvidsson, S., & Spjuth, O. (2021) · Xenobiotica · doi:10.1080/00498254.2021.2011471 · Cited by 25 · see citing works

Assessing the calibration in toxicological in vitro models with conformal prediction

Morger, A., Svensson, F., Arvidsson, S., Gauraha, N., Norinder, U., Spjuth, O., & Volkamer, A. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00511-5 · Cited by 24 · see citing works

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study

Yang, M., Chen, H., Hu, W., Mischi, M., Shan, C., Li, J., Long, X., & Liu, C. (2024) · Journal of Medical Internet Research · doi:10.2196/50369 · Cited by 24 · see citing works

Development and Evaluation of Conformal Prediction Methods for Quantitative Structure–Activity Relationship

Xu, Y., Liaw, A. I., Sheridan, R. P., & Svetnik, V. (2024) · ACS Omega · doi:10.1021/acsomega.4c02017 · Cited by 20 · see citing works

Autonomous AI Agent for QSAR Modeling with Dataset Curation, Descriptor Selection, and Domain Assessment

Chen, H., Liu, F., & Lin, Z. (2025) · Pharmacophore · doi:10.51847/bhyi6rjara · Cited by 18 · see citing works

A Transparent and Valid Framework for Rockburst Assessment: Unifying Interpretable Machine Learning and Conformal Prediction

Ibrahim, B., Tetteh-Asare, A., & Ahenkorah, I. (2024) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-024-03847-0 · Cited by 16 · see citing works

Reproducible WiSDM: a workflow for reproducible invasive alien species risk maps under climate change scenarios using standardized open data

Davis, A. J. S., Groom, Q., Adriaens, T., Vanderhoeven, S., Troch, R. D., Oldoni, D., Desmet, P., Reyserhove, L., Lens, L., & Strubbe, D. (2024) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2024.1148895 · Cited by 16 · see citing works

Machine learning with asymmetric abstention for biomedical decision-making

Gandouz, M., Holzmann, H., & Heider, D. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01655-y · Cited by 15 · see citing works

Calibrating probabilistic predictions of quantile regression forests with conformal predictive systems

Wang, D., Wang, P., Wang, C., & Wang, P. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.02.003 · Cited by 15 · see citing works

Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data

Morger, A., de Lomana, M. G., Norinder, U., Svensson, F., Kirchmair, J., Mathea, M., & Volkamer, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-09309-3 · Cited by 15 · see citing works

A methodology to correctly assess the applicability domain of cell membrane permeability predictors for cyclic peptides

Geylan, G., Maria, L. D., Engkvist, O., David, F., & Norinder, U. (2024) · Digital Discovery · doi:10.1039/d4dd00056k · Cited by 13 · see citing works

Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in combination with in silico predictions

Klutzny, S., Kornhuber, M., Morger, A., Schönfelder, G., Volkamer, A., Oelgeschläger, M., & Dunst, S. (2021) · Environment International · doi:10.1016/j.envint.2021.106947 · Cited by 14 · see citing works

Reliable machine learning models in genomic medicine using conformal prediction

Papangelou, C., Kyriakidis, K., Natsiavas, P., Chouvarda, I., & Malousi, A. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1507448 · Cited by 12 · see citing works

Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design

van den Maagdenberg, H. W., van Otterloo, J. D. M., van Hasselt, J. G. C., van der Graaf, P. H., & van Westen, G. J. P. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00107 · Cited by 12 · see citing works

Reliable anti-cancer drug sensitivity prediction and prioritization

Lenhof, K., Eckhart, L., Rolli, L.-M., Volkamer, A., & Lenhof, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-62956-6 · Cited by 11 · see citing works

Machine Learning Strategies When Transitioning between Biological Assays

Arvidsson, S., Ahlberg, E., Noeske, T., & Spjuth, O. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00293 · Cited by 11 · see citing works

Applying regression conformal prediction with nearest neighbors to time series data

Tajmouati, S., Wahbi, B. E., & Dakkon, M. (2022) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2022.2057538 · Cited by 11 · see citing works

In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology

Fagerholm, U., Hellberg, S., Alvarsson, J., & Spjuth, O. (2022) · Xenobiotica · doi:10.1080/00498254.2022.2049397 · Cited by 11 · see citing works

How to predict effective drug combinations – moving beyond synergy scores

Eckhart, L., Lenhof, K., Herrmann, L., Rolli, L.-M., & Lenhof, H. (2025) · iScience · doi:10.1016/j.isci.2025.112622 · Cited by 11 · see citing works

GeoConformal Prediction: A Model-Agnostic Framework for Measuring the Uncertainty of Spatial Prediction

Lou, X., Luo, P., & Meng, L. (2025) · Annals of the American Association of Geographers · doi:10.1080/24694452.2025.2516091 · Cited by 15 · see citing works

Synthesis and anticancer evaluation of 6-azacyclonol-2,4,6-trimethylpyridin-3-ol derivatives: M3 muscarinic acetylcholine receptor-mediated anticancer activity of a cyclohexyl derivative in androgen-refractory prostate cancer

Karmacharya, U., Chaudhary, P., Lim, D., Dahal, S., Awasthi, B. P., Park, H. D., Kim, J., & Jeong, B. (2021) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2021.104805 · Cited by 10 · see citing works

In Silico Predictions of the Gastrointestinal Uptake of Macrocycles in Man Using Conformal Prediction Methodology

Fagerholm, U., Hellberg, S., Alvarsson, J., & Spjuth, O. (2022) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2022.05.010 · Cited by 10 · see citing works

MBSSA-Bi-AESN: Classification prediction of bi-directional adaptive echo state network based on modified binary salp swarm algorithm and feature selection

Wu, X., Zhan, J., Li, T., Ding, W., & Pedrycz, W. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05280-w · Cited by 10 · see citing works

iSKIN: Integrated application of machine learning and Mondrian conformal prediction to detect skin sensitizers in cosmetic raw materials

Kong, W., Zhu, J., Shan, P., Ying, H., Chen, T., Zhang, B., Peng, C., Wang, Z., Wang, Y., Huang, L., et al. (2024) · SmartMat · doi:10.1002/smm2.1278 · Cited by 9 · see citing works

Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence

Urbina, F., Jones, T., Harris, J. S., Snyder, S. H., Lane, T. R., & Ekins, S. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00405 · Cited by 9 · see citing works

Evaluating Critical Success Factors in AI-Driven Drug Discovery Using AHP: A Strategic Framework for Optimization

Talib, A. M., Al-Hgaish, A. M., Atan, R. B., Alshammari, A., Alomary, F. O., Yaakob, R., Alsahli, A., & Osman, M. H. (2025) · IEEE Access · doi:10.1109/access.2025.3546925 · Cited by 10 · see citing works

High-Throughput Physiologically Based Pharmacokinetic Model for Rodent Pharmacokinetics Prediction Using Machine Learning-Predicted Inputs and a Large In Vivo Pharmacokinetics Data Set

Bassani, D., Morger, A., Zhang, J. D., Docci, L., Cecere, G., Pähler, A., Belubbi, T., Laye, P., Shih, I., & Parrott, N. J. (2026) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5c01317 · Cited by 9 · see citing works

Functional protein mining with conformal guarantees

Boger, R. S., Chithrananda, S., Angelopoulos, A. N., Yoon, P. H., Jordan, M. I., & Doudna, J. A. (2025) · Nature Communications · doi:10.1038/s41467-024-55676-y · Cited by 9 · see citing works

Conformal efficiency as a metric for comparative model assessment befitting federated learning

Heyndrickx, W., Arany, A., Simm, J., Pentina, A., Sturm, N., Humbeck, L., Mervin, L. H., Zalewski, A., Oldenhof, M., Schmidtke, P., et al. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100070 · Cited by 10 · see citing works

AI-powered programmable virtual humans toward human physiologically-based drug discovery

Wu, Y., Bourne, P. E., & Xie, L. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104497 · Cited by 8 · see citing works

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models

Kopańska, K., Rodríguez-Belenguer, P., Llopis‐Lorente, J., Trénor, B., Sáiz, J., & Pastor, M. (2023) · Archives of Toxicology · doi:10.1007/s00204-023-03557-6 · Cited by 8 · see citing works

Dynamic applicability domain (dAD): compound–target binding affinity estimates with local conformal prediction

Oršolić, D., & Šmuc, T. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad465 · Cited by 8 · see citing works

Rapid Traversal of Ultralarge Chemical Space using Machine Learning Guided Docking ScreensPreprint

Luttens, A., de Vaca, I. C., Sparring, L., Norinder, U., & Carlsson, J. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-w3x36 · Cited by 8 · see citing works

Advancements in Rational Multi‐Targeted Drug Discovery

Ravikumar, B., Cichońska, A., Sahni, N., Aittokallio, T. A., & Rahman, R. (2024) · Polypharmacology · doi:10.1002/9781394182862.ch9 · Cited by 8 · see citing works

Conformal prediction-based machine learning in Cheminformatics: Current applications and new challenges

Astigarraga, M., Sánchez-Ruiz, A., & Colmenarejo, G. (2025) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2025.100127 · Cited by 9 · see citing works

Capturing uncertainty in black-box chromatography modelling using conformal prediction and Gaussian processes

Pham, T., Bassett, R., & Aickelin, U. (2025) · Computers and Chemical Engineering · doi:10.1016/j.compchemeng.2025.109136 · Cited by 8 · see citing works

Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology

Badkul, A., Xie, L., Zhang, S., & Xie, L. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01151-2 · Cited by 7 · see citing works

Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

Norinder, U., Spjuth, O., & Svensson, F. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00555-7 · Cited by 7 · see citing works

Using the ANDROMEDA by Prosilico Software for Prediction of the Human Pharmacokinetics of 4 Compounds of Natural Origin - Colistin, Curucumin, UCN-01 and VoclosporinPreprint

Fagerholm, U., Hellberg, S., Alvarsson, J., & Spjuth, O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.17.504228 · Cited by 7 · see citing works

Distance-Aware Molecular Property Prediction in Nonlinear Structure–Property Space

Kim, J. Y., & Vlachos, D. G. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01037 · Cited by 6 · see citing works

Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts

Hernández-Hernández, S., Guo, Q., & Ballester, P. J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112501 · Cited by 6 · see citing works

The intracellular free concentration of endocrine disrupting chemicals enables translation between cell-free and cell-based estrogenic activity assays

Kos, V. M., Arvidsson, S., Islam, B., Nikiforova, V., Mickols, E., Meyer, A., Svensson, R., Boztepe, Ü. G., Banti, E., Lundquist, P., et al. (2025) · Environmental Toxicology and Pharmacology · doi:10.1016/j.etap.2025.104750 · Cited by 4 · see citing works

Conformal Selection for Efficient and Accurate Compound Screening in Drug Discovery

Bai, T., Tang, P., Xu, Y., Svetnik, V., Yang, B., Khalili, A., Yu, X., & Yang, A. Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01633 · Cited by 3 · see citing works

Predicting protein network topology clusters from chemical structure using deep learning

Sreenivasan, A. P., Harrison, P. J., Schaal, W., Matuszewski, D. J., Kultima, K., & Spjuth, O. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00622-7 · Cited by 4 · see citing works

Enabling Data-Driven Solubility Modeling at GSK: Enhancing Purge Predictions for Mutagenic Impurities

Vià, L. D., Depoortere, M., Willacy, R. D., Roberts, A. J., Sokkar, P., Fossepré, M., Ruba, A. A., & Zwierzyna, M. (2024) · Organic Process Research & Development · doi:10.1021/acs.oprd.4c00384 · Cited by 4 · see citing works

Uncertainty Quantification of Multimodal Models

Obayemi, A., & Nguyen, K. A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8889-0_23 · Cited by 4 · see citing works

Disease phenotype prediction in multiple sclerosis

Herman, S., Arvidsson, S., Zjukovskaja, C., Khoonsari, P. E., Svenningsson, A., Burman, J., Spjuth, O., & Kultima, K. (2023) · iScience · doi:10.1016/j.isci.2023.106906 · Cited by 5 · see citing works

On the early detection of Sepsis in MIMIC-III

Medina, M., & Sala, P. (2021) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi52183.2021.00035 · Cited by 4 · see citing works

Comparing Lipinskis Rule of 5 and Machine Learning Based Prediction of Fraction Absorbed for Assessing Oral Absorption in HumansPreprint

Fagerholm, U., Hellberg, S., Alvarsson, J., Ekmefjord, M., & Spjuth, O. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.20.608791 · Cited by 4 · see citing works

Scupa: single-cell unified polarization assessment of immune cells using the single-cell foundation model

Liu, W., & Zhao, Z. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf090 · Cited by 3 · see citing works

High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals

Clarke, N., Urchs, S., Nguyen, H., Moreau, C. A., Dansereau, C., Tam, A., Evans, A. C., & Bellec, L. (2025) · GigaScience · doi:10.1093/gigascience/giaf091 · Cited by 2 · see citing works

A Hybrid-Based Deep Learning Framework for Pill Detection, Multi-Attribute Classification, and Prediction With Metadata Retrieval

Sachin, A. S., & Karthikeyan, J. (2026) · IEEE Access · doi:10.1109/access.2026.3654582 · Cited by 3 · see citing works

Conformal Prediction for Counterfactual Detection in Concept Learning from Synthetic Visual Patterns

Norinder, U., Lowry, S. M., Müller, H., & Holzinger, A. (2026) · Electronics · doi:10.3390/electronics15071346 · Cited by 3 · see citing works

Predicting Larch Casebearer damage with confidence using Yolo network models and conformal prediction

Norinder, U., & Lowry, S. M. (2023) · Remote Sensing Letters · doi:10.1080/2150704x.2023.2258460 · Cited by 3 · see citing works

Prediction and Classification of the Uptake and Disposition of Antidepressants and New CNS-Active Drugs in the Human Brain using the ANDROMEDA by Prosilico Software and Brainavailability-MatrixPreprint

Fagerholm, U., Hellberg, S., Alvarsson, J., & Spjuth, O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.28.509936 · Cited by 3 · see citing works

Validation of predicted conformal intervals for prediction of human clinical pharmacokineticsPreprint

Fagerholm, U., Alvarsson, J., Hellberg, S., & Spjuth, O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.10.515917 · Cited by 3 · see citing works

Predicting in vitro assays related to liver function using probabilistic machine learning

Morelli, F. M., Raschke, M., Jungmann, N. A., Bairlein, M., & de Lomana, M. G. (2025) · Toxicology · doi:10.1016/j.tox.2025.154195 · Cited by 2 · see citing works

Error Reduction in Leukemia Machine Learning Classification With Conformal Prediction

Wiklander, M. L., Zachariah, D., Krali, O., & Nordlund, J. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00324 · Cited by 2 · see citing works

Coronary artery disease diagnosis by integrating conformal prediction with a multi-objective evolutionary algorithm

Afzali, A. M., Maleki, S., Tavakkoli-Moghaddam, R. R., Bozorgi-Amiri, A., & Tafakkori, K. (2025) · Health and Technology · doi:10.1007/s12553-025-01007-0 · Cited by 2 · see citing works

QbD-driven optimization of ondansetron HCl via solid dispersion-loaded orodispersible films for chemotherapy-induced nausea and vomiting (CINV) management

Afrose, N., Sahoo, S., Chakraborty, R., Bhowmick, P., & Bhowmick, M. (2026) · Journal of Dispersion Science and Technology · doi:10.1080/01932691.2026.2618053 · Cited by 2 · see citing works

Prediction of Biopharmaceutical Characteristics of PROTACs using the ANDROMEDA by Prosilico SoftwarePreprint

Fagerholm, U., Hellberg, S., Alvarsson, J., & Spjuth, O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.22.509053 · Cited by 2 · see citing works

Reliability and Applicability Assessment for Machine Learning Models

Urbina, F., & Ekins, S. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch13 · Cited by 2 · see citing works

Target‐Engagement Prediction From Molecular Structure, In Vitro and Animal Data: A Tutorial for Early Assessment of Clinical Drug Exposure and Therapeutic/Adverse Potential Using the ANDROMEDA and PLATO Software on the InSilicoTrials Platform

Totis, N., Sips, F. L. P., Catozzi, S., Bisotti, M.-A., Bambury, P., Stathopoulos, S., Carbone, V., Emili, L., Torchia, M., Belgiovine, V., et al. (2026) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.70353 · Cited by 1 · see citing works

Expanding the H295R steroidogenic assay using LC-MS/MS and an ER-alpha reporter gene assay as read-outs using azole fungicides as test compounds

Vazakidou, P., Koopmans, C., Grimberg, S., Evangelista, S., Koekkoek, J. C., Lamoree, M. H., Leonards, P. E. G., & van Duursen, M. B. M. (2021) · Toxicology Letters · doi:10.1016/s0378-4274(21)00401-x · Cited by 1 · see citing works

Data-Efficient Handling of Temporary Uncertainties in Online Alarm Flood Classification

Manca, G., Kunze, F. C., & Fay, A. (2025) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps65515.2025.11087828 · Cited by 1 · see citing works

Confidence-Aware Fine-Tuning of Sequential Recommendation Systems Via Conformal Prediction

Wang, C., Wang, F., Guo, R., Liang, Y., & Yu, P. S. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401637 · Cited by 4 · see citing works

A continuous in silico learning strategy to identify safety liabilities in compounds used in the leather and textile industry

March-Vila, E., Ferretti, G., Terricabras, E., Ardao, I., Brea, J. M., Varela, M. J., Arana, Á. J., Rubiolo, J. A., Sanz, F., Loza, M. I., et al. (2023) · Archives of Toxicology · doi:10.1007/s00204-023-03459-7 · Cited by 1 · see citing works

Reliable Anti-Cancer Drug Sensitivity Prediction and PrioritizationPreprint

Lenhof, K., Eckhart, L., Rolli, L.-M., Volkamer, A., & Lenhof, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3542373/v1 · Cited by 1 · see citing works

Reliable Anti-Cancer Drug Sensitivity Prediction and PrioritizationPreprint

Lenhof, K., Eckhart, L., Rolli, L.-M., Volkamer, A., & Lenhof, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3542373/v2 · Cited by 1 · see citing works

Dynamic applicability domain ( d AD) for compound-target binding affinity prediction task with confidence guaranteesPreprint

Oršolić, D., & Šmuc, T. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.22.504786

Application of the ANDROMEDA Software for Prediction of the Human Pharmacokinetics of Modern Anticancer DrugsPreprint

Fagerholm, U., Hellberg, S., Alvarsson, J., & Spjuth, O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.18.533259

Prediction of the Human Pharmacokinetics of 30 Modern Antibiotics Using the ANDROMEDA SoftwarePreprint

Fagerholm, U., Hellberg, S., Alvarsson, J., & Spjuth, O. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.28.534601

BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability

Chang, X., Dost, K., Zhao, K., Demontis, A., Roli, F., Dobbie, G., & Wicker, J. S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-33374-3_1

Uncertainty measurement for complex event prediction in safety-critical systemsPreprint

Peixoto, M. J. P., & Azim, A. (2023) · Research Square · doi:10.21203/rs.3.rs-3237791/v1

Reliable Anti-Cancer Drug Sensitivity Prediction and PrioritizationPreprint

Lenhof, K., Eckhart, L., Rolli, L.-M., Volkamer, A., & Lenhof, H. (2024) · Research Square · doi:10.21203/rs.3.rs-3542373/v3

Mondrian inductive conformal classifier for the quantitative structure–odor relationship to estimate prediction confidence

Debnath, T., Oishi, K., & Nakabayashi, A. (2024) · IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) · doi:10.1109/isoen61239.2024.10555997

Development and Implementation of Anartificial Intelligence-Based Model for Predicting Drug SalesPreprint

Xiong, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5010591

HCS-DFC: A Diffusion Classifier for Mode of Action Prediction Using Morphological Profiles

Kosciukiewicz, J., Rymarczyk, D., & Zielinski, B. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00408

Neural network custom loss function for flexible and high-quality prediction intervals

Wang, H., & Wang, D. (2025) · Mathematical Foundations of Computing · doi:10.3934/mfc.2025039

Additional file 1 of Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

Norinder, U., Spjuth, O., & Fredrik, S. (2021) · Figshare · doi:10.6084/m9.figshare.16726850

Additional file 1 of Machine learning with asymmetric abstention for biomedical decision-making

Gandouz, M., Holzmann, H., & Heider, D. (2021) · Figshare · doi:10.6084/m9.figshare.16880413

Additional file 1 of Assessing the calibration in toxicological in vitro models with conformal prediction

Morger, A., Svensson, F., Arvidsson, S., Gauraha, N., Norinder, U., Spjuth, O., & Volkamer, A. (2021) · Figshare · doi:10.6084/m9.figshare.14516380

Application of Conformal Prediction in QSAR

Eklund, M., Norinder, U., Boyer, S. K., & Carlsson, L. (2012) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-3-642-33412-2_17 · Cited by 29 · see citing works

The application of conformal prediction to the drug discovery process

Eklund, M., Norinder, U., Boyer, S. K., & Carlsson, L. (2013) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-013-9378-2 · Cited by 73 · see citing works

Beyond Heuristics: A Model-Agnostic Framework for Uncertainty Quantification in QSAR via Adaptive Conformal Prediction

Jeliazkova, N., Kochev, N. T., Iliev, L., & Jeliazkov, V. (2026) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.6c00065 · Cited by 1 · see citing works

Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination

Norinder, U., Carlsson, L., Boyer, S. K., & Eklund, M. (2015) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2014.12.021 · Cited by 34 · see citing works

Development and Evaluation of Conformal Prediction Methods for QSARPreprint

Xu, Y., Liaw, A. I., Sheridan, R. P., & Svetnik, V. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.00970