Predicting With Confidence: Using Conformal Prediction in Drug Discovery

Scikit-learn: Machine Learning in Python

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

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Cross-conformal predictors

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

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

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Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction

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Graph Neural Networks in Modern AI-Aided Drug Discovery

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Predictive Modeling in Medicine

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Assigning confidence to molecular property prediction

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Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

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

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Validation guidelines for drug-target prediction methods

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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 48

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

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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 42

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

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

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

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

Wind speed forecasting approach using conformal prediction and feature importance selection

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Chemoinformatic regression methods and their applicability domain

Dutschmann, T., Schlenker, V., & Baumann, K. (2024) · Molecular Informatics · doi:10.1002/minf.202400018 · Cited by 31

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

Assessing the calibration in toxicological in vitro models with conformal prediction

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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

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

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

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

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

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

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

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 18

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

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

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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

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 13

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

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

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

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

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 12

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

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

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

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

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

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Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence

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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

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

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Functional protein mining with conformal guarantees

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

Heyndrickx, W., Arany, Á., Simm, J., Pentina, A., Sturm, N., Humbeck, L., Mervin, L., Zalewski, A., Oldenhof, M., Schmidtke, P., et al. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2023.100070 · Cited by 9

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

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

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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

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

Advancements in Rational Multi‐Targeted Drug Discovery

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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 10

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

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

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

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

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

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 8

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

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

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

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

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

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

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

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

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

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

Clarke, N., Urchs, S. G. W., Nguyen, H. D., Moreau, C. A., Dansereau, C. L., Tam, A., Evans, A. C., & Bellec, P. (2025) · GigaScience · doi:10.1093/gigascience/giaf091 · Cited by 3

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

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

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

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

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

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

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

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

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

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

Reliability and Applicability Assessment for Machine Learning Models

Urbina, F., & Ekins, S. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch13 · Cited by 3

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

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

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

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

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

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

Reliable Anti-Cancer Drug Sensitivity Prediction and PrioritizationPreprint

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

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

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

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

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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

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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

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Application of Conformal Prediction in QSAR

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Development and Evaluation of Conformal Prediction Methods for QSARPreprint

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