1
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Lenhof K, Eckhart L, Rolli LM, Volkamer A, Lenhof HP. Reliable anti-cancer drug sensitivity prediction and prioritization. Sci Rep 2024; 14:12303. [PMID: 38811639 PMCID: PMC11137046 DOI: 10.1038/s41598-024-62956-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/23/2024] [Indexed: 05/31/2024] Open
Abstract
The application of machine learning (ML) to solve real-world problems does not only bear great potential but also high risk. One fundamental challenge in risk mitigation is to ensure the reliability of the ML predictions, i.e., the model error should be minimized, and the prediction uncertainty should be estimated. Especially for medical applications, the importance of reliable predictions can not be understated. Here, we address this challenge for anti-cancer drug sensitivity prediction and prioritization. To this end, we present a novel drug sensitivity prediction and prioritization approach guaranteeing user-specified certainty levels. The developed conformal prediction approach is applicable to classification, regression, and simultaneous regression and classification. Additionally, we propose a novel drug sensitivity measure that is based on clinically relevant drug concentrations and enables a straightforward prioritization of drugs for a given cancer sample.
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Affiliation(s)
- Kerstin Lenhof
- Center for Bioinformatics, Chair for Bioinformatics, Saarland Informatics Campus (E2.1) Saarland University, Campus, 66123, Saarbrücken, Saarland, Germany.
| | - Lea Eckhart
- Center for Bioinformatics, Chair for Bioinformatics, Saarland Informatics Campus (E2.1) Saarland University, Campus, 66123, Saarbrücken, Saarland, Germany
| | - Lisa-Marie Rolli
- Center for Bioinformatics, Chair for Bioinformatics, Saarland Informatics Campus (E2.1) Saarland University, Campus, 66123, Saarbrücken, Saarland, Germany
| | - Andrea Volkamer
- Center for Bioinformatics, Chair for Data Driven Drug Design, Saarland Informatics Campus (E2.1) Saarland University, Campus, 66123, Saarbrücken, Saarland, Germany
| | - Hans-Peter Lenhof
- Center for Bioinformatics, Chair for Bioinformatics, Saarland Informatics Campus (E2.1) Saarland University, Campus, 66123, Saarbrücken, Saarland, Germany
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2
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Backenköhler M, Groß J, Wolf V, Volkamer A. Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases. J Chem Inf Model 2024; 64:4009-4020. [PMID: 38751014 DOI: 10.1021/acs.jcim.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2024]
Abstract
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand complex structures. Exemplified for kinase drug discovery, we address this issue by generating kinase-ligand complex data using template docking for the kinase compound subset of available ChEMBL assay data. To evaluate the benefit of the created complex data, we use it to train a structure-based E(3)-invariant graph neural network. Our evaluation shows that binding affinities can be predicted with significantly higher precision by models that take synthetic binding poses into account compared to ligand- or drug-target interaction models alone.
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Affiliation(s)
- Michael Backenköhler
- Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken 66123, Germany
| | - Joschka Groß
- Modeling and Simulation, Saarland University, Saarbrücken 66123, Germany
| | - Verena Wolf
- Modeling and Simulation, Saarland University, Saarbrücken 66123, Germany
| | - Andrea Volkamer
- Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken 66123, Germany
- Structural Bioinformatics and in Silico Toxicology Institute of Physiology, Universitätsmedizin Berlin, Berlin 10117, Germany
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3
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Bassani D, Brigo A, Andrews-Morger A. Federated Learning in Computational Toxicology: An Industrial Perspective on the Effiris Hackathon. Chem Res Toxicol 2023; 36:1503-1517. [PMID: 37584277 PMCID: PMC10523574 DOI: 10.1021/acs.chemrestox.3c00137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Indexed: 08/17/2023]
Abstract
In silico approaches have acquired a towering role in pharmaceutical research and development, allowing laboratories all around the world to design, create, and optimize novel molecular entities with unprecedented efficiency. From a toxicological perspective, computational methods have guided the choices of medicinal chemists toward compounds displaying improved safety profiles. Even if the recent advances in the field are significant, many challenges remain active in the on-target and off-target prediction fields. Machine learning methods have shown their ability to identify molecules with safety concerns. However, they strongly depend on the abundance and diversity of data used for their training. Sharing such information among pharmaceutical companies remains extremely limited due to confidentiality reasons, but in this scenario, a recent concept named "federated learning" can help overcome such concerns. Within this framework, it is possible for companies to contribute to the training of common machine learning algorithms, using, but not sharing, their proprietary data. Very recently, Lhasa Limited organized a hackathon involving several industrial partners in order to assess the performance of their federated learning platform, called "Effiris". In this paper, we share our experience as Roche in participating in such an event, evaluating the performance of the federated algorithms and comparing them with those coming from our in-house-only machine learning models. Our aim is to highlight the advantages of federated learning and its intrinsic limitations and also suggest some points for potential improvements in the method.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Alessandro Brigo
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
| | - Andrea Andrews-Morger
- Pharmaceutical Research &
Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
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4
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Morger A, Garcia de Lomana M, Norinder U, Svensson F, Kirchmair J, Mathea M, Volkamer A. Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data. Sci Rep 2022; 12:7244. [PMID: 35508546 PMCID: PMC9068909 DOI: 10.1038/s41598-022-09309-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 03/17/2022] [Indexed: 11/09/2022] Open
Abstract
Machine learning models are widely applied to predict molecular properties or the biological activity of small molecules on a specific protein. Models can be integrated in a conformal prediction (CP) framework which adds a calibration step to estimate the confidence of the predictions. CP models present the advantage of ensuring a predefined error rate under the assumption that test and calibration set are exchangeable. In cases where the test data have drifted away from the descriptor space of the training data, or where assay setups have changed, this assumption might not be fulfilled and the models are not guaranteed to be valid. In this study, the performance of internally valid CP models when applied to either newer time-split data or to external data was evaluated. In detail, temporal data drifts were analysed based on twelve datasets from the ChEMBL database. In addition, discrepancies between models trained on publicly-available data and applied to proprietary data for the liver toxicity and MNT in vivo endpoints were investigated. In most cases, a drastic decrease in the validity of the models was observed when applied to the time-split or external (holdout) test sets. To overcome the decrease in model validity, a strategy for updating the calibration set with data more similar to the holdout set was investigated. Updating the calibration set generally improved the validity, restoring it completely to its expected value in many cases. The restored validity is the first requisite for applying the CP models with confidence. However, the increased validity comes at the cost of a decrease in model efficiency, as more predictions are identified as inconclusive. This study presents a strategy to recalibrate CP models to mitigate the effects of data drifts. Updating the calibration sets without having to retrain the model has proven to be a useful approach to restore the validity of most models.
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Affiliation(s)
- Andrea Morger
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Berlin, 10117, Germany
| | - Marina Garcia de Lomana
- BASF SE, 67056, Ludwigshafen, Germany
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, University of Vienna, Vienna, 1090, Austria
| | - Ulf Norinder
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, 751 24, Sweden
- Dept Computer and Systems Sciences, Stockholm University, Kista, 164 07, Sweden
- MTM Research Centre, School of Science and Technology, 701 82, Örebro, Sweden
| | - Fredrik Svensson
- Alzheimer's Research UK UCL Drug Discovery Institute, London, WC1E 6BT, UK
| | - Johannes Kirchmair
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, University of Vienna, Vienna, 1090, Austria
| | | | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Berlin, 10117, Germany.
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Fagerholm U, Hellberg S, Alvarsson J, Spjuth O. In silico predictions of the human pharmacokinetics/toxicokinetics of 65 chemicals from various classes using conformal prediction methodology. Xenobiotica 2022; 52:113-118. [PMID: 35238270 DOI: 10.1080/00498254.2022.2049397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Pharmacokinetic/toxicokinetic (PK/TK) information for chemicals in humans is generally lacking. Here we applied machine learning, conformal prediction and a new physiologically-based PK/TK model for prediction of the human PK/TK of 65 chemicals from different classes, including carcinogens, food constituents and preservatives, vitamins, sweeteners, dyes and colours, pesticides, alternative medicines, flame retardants, psychoactive drugs, dioxins, poisons, UV-absorbents, surfactants, solvents and cosmetics.About 80% of the main human PK/TK (fraction absorbed, oral bioavailability, half-life, unbound fraction in plasma, clearance, volume of distribution, fraction excreted) for the selected chemicals was missing in the literature. This information was now added (from in silico predictions). Median and mean prediction errors for these parameters were 1.3- to 2.7-fold and 1.4- to 4.8-fold, respectively. In total, 59 and 86% of predictions had errors <2- and <5-fold, respectively. Predicted and observed PK/TK for the chemicals was generally within the range for pharmaceutical drugs.The results validated the new integrated system for prediction of the human PK/TK for different chemicals and added important missing information. No general difference in PK/TK-characteristics was found between the selected chemicals and pharmaceutical drugs.
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Affiliation(s)
| | | | - Jonathan Alvarsson
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, Uppsala, SE-751 24 Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, Uppsala, 75124 Sweden
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Klutzny S, Kornhuber M, Morger A, Schönfelder G, Volkamer A, Oelgeschläger M, Dunst S. Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in combination with in silico predictions. ENVIRONMENT INTERNATIONAL 2022; 158:106947. [PMID: 34717173 DOI: 10.1016/j.envint.2021.106947] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Exposure to environmental chemicals that interfere with normal estrogen function can lead to adverse health effects, including cancer. High-throughput screening (HTS) approaches facilitate the efficient identification and characterization of such substances. OBJECTIVES We recently described the development of the E-Morph Assay, which measures changes at adherens junctions as a clinically-relevant phenotypic readout for estrogen receptor (ER) alpha signaling activity. Here, we describe its further development and application for automated robotic HTS. METHODS Using the advanced E-Morph Screening Assay, we screened a substance library comprising 430 toxicologically-relevant industrial chemicals, biocides, and plant protection products to identify novel substances with estrogenic activities. Based on the primary screening data and the publicly available ToxCast dataset, we performed an insilico similarity search to identify further substances with potential estrogenic activity for follow-up hit expansion screening, and built seven insilico ER models using the conformal prediction (CP) framework to evaluate the HTS results. RESULTS The primary and hit confirmation screens identified 27 'known' estrogenic substances with potencies correlating very well with the published ToxCast ER Agonist Score (r=+0.95). We additionally detected potential 'novel' estrogenic activities for 10 primary hit substances and for another nine out of 20 structurally similar substances from insilico predictions and follow-up hit expansion screening. The concordance of the E-Morph Screening Assay with the ToxCast ER reference data and the generated CP ER models was 71% and 73%, respectively, with a high predictivity for ER active substances of up to 87%, which is particularly important for regulatory purposes. DISCUSSION These data provide a proof-of-concept for the combination of in vitro HTS approaches with insilico methods (similarity search, CP models) for efficient analysis of large substance libraries in order to prioritize substances with potential estrogenic activity for subsequent testing against higher tier human endpoints.
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Affiliation(s)
- Saskia Klutzny
- Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany
| | - Marja Kornhuber
- Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany; Freie Universität Berlin, Berlin, Germany
| | - Andrea Morger
- In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Gilbert Schönfelder
- Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany; Institute of Clinical Pharmacology and Toxicology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Andrea Volkamer
- In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Michael Oelgeschläger
- Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany
| | - Sebastian Dunst
- Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany.
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Norinder U, Spjuth O, Svensson F. Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning. J Cheminform 2021; 13:77. [PMID: 34600569 PMCID: PMC8487527 DOI: 10.1186/s13321-021-00555-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 09/15/2021] [Indexed: 12/05/2022] Open
Abstract
Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox.
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Affiliation(s)
- Ulf Norinder
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-75124, Uppsala, Sweden.,Department of Computer and Systems Sciences, Stockholm University, Box 7003, 164 07, Kista, Sweden.,MTM Research Centre, School of Science and Technology, Örebro University, 70182, Örebro, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-75124, Uppsala, Sweden.
| | - Fredrik Svensson
- Alzheimer's Research UK UCL Drug Discovery Institute, University College London, The Cruciform Building, Gower Street, London, WC1E 6BT, UK.
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Arvidsson McShane S, Ahlberg E, Noeske T, Spjuth O. Machine Learning Strategies When Transitioning between Biological Assays. J Chem Inf Model 2021; 61:3722-3733. [PMID: 34152755 PMCID: PMC8317157 DOI: 10.1021/acs.jcim.1c00293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a retrospective study, we here explore different modeling strategies of how to combine data from the old and new assays when training conformal prediction models using data from hERG and NaV assays. We suggest to continuously monitor the validity and efficiency of models as more data is accumulated from the new assay and select a modeling strategy based on these metrics. In order to maximize the utility of data from the old assay, we propose a strategy that augments the proper training set of an inductive conformal predictor by adding data from the old assay but only having data from the new assay in the calibration set, which results in valid (well-calibrated) models with improved efficiency compared to other strategies. We study the results for varying sizes of new and old assays, allowing for discussion of different practical scenarios. We also conclude that our proposed assay transition strategy is more beneficial, and the value of data from the new assay is higher, for the harder case of regression compared to classification problems.
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Affiliation(s)
- Staffan Arvidsson McShane
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden
| | - Ernst Ahlberg
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden.,Stena Line Scandinavia AB, AI & Data, 405 19 Gothenburg, Sweden.,Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, 431 50 Gothenburg, Sweden
| | - Tobias Noeske
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, 431 50 Gothenburg, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, 751 24 Uppsala, Sweden
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