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Tan Z, Zhao Y, Lin K, Zhou T. Multi-task pretrained language model with novel application domains enables more comprehensive health and ecological toxicity prediction. JOURNAL OF HAZARDOUS MATERIALS 2024; 477:135265. [PMID: 39038381 DOI: 10.1016/j.jhazmat.2024.135265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/29/2024] [Accepted: 07/18/2024] [Indexed: 07/24/2024]
Abstract
In silico models for screening substances of healthy and ecological concern are essential for effective chemical management. However, current data-driven toxicity prediction models confront formidable challenges related to expressive capacity, data scarcity, and reliability issues. Thus, this study introduces TOX-BERT, a SMILES-based pretrained model for screening health and ecological toxicity. Results show that masked atom recovery pretraining and multi-task learning offer promising solutions to enhance model capacity and address data scarcity issues. Two novel application domain (AD) parameters, termed PCA-AD and LDS, were proposed to improve prediction reliability of TOX-BERT with accuracy surpassing 90 % and mean absolute error (MAE) below 0.52. TOX-BERT was applied to 18,905 IECSC chemicals, revealing distinct toxicity relationships that align with experimental studies such as those between cardiotoxicity and acute ecotoxicity. In addition to previous PBT screening, 156 potential high-risk chemicals for specific endpoint were identified covering 7 categories. Furthermore, a SMILES-based toxicity site detection approach was developed for structural toxicity analysis. These advancements carry profound implications to address challenges faced by current data-driven toxicity prediction models. TOX-BERT emerges as a valuable tool for more comprehensive, reliable, and applicable predictions of health and ecological toxicity in chemical risk assessment and management.
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Affiliation(s)
- Zhichao Tan
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, 1515 North Zhongshan Rd. (No. 2), Shanghai 200092, PR China.
| | - Youcai Zhao
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, 1515 North Zhongshan Rd. (No. 2), Shanghai 200092, PR China.
| | - Kunsen Lin
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, 1515 North Zhongshan Rd. (No. 2), Shanghai 200092, PR China.
| | - Tao Zhou
- The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, 1515 North Zhongshan Rd. (No. 2), Shanghai 200092, PR China.
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2
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Odje F, Meijer D, von Coburg E, van der Hooft JJJ, Dunst S, Medema MH, Volkamer A. Unleashing the potential of cell painting assays for compound activities and hazards prediction. FRONTIERS IN TOXICOLOGY 2024; 6:1401036. [PMID: 39086553 PMCID: PMC11288911 DOI: 10.3389/ftox.2024.1401036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 06/14/2024] [Indexed: 08/02/2024] Open
Abstract
The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards in drug discovery and toxicology. CP enables the rapid, multiplexed investigation of various molecular mechanisms for thousands of compounds at the single-cell level. The resulting large volumes of image data provide great opportunities but also pose challenges to image and data analysis routines as well as property prediction models. This review addresses the integration of CP-based phenotypic data together with or in substitute of structural information from compounds into machine (ML) and deep learning (DL) models to predict compound activities for various human-relevant disease endpoints and to identify the underlying modes-of-action (MoA) while avoiding unnecessary animal testing. The successful application of CP in combination with powerful ML/DL models promises further advances in understanding compound responses of cells guiding therapeutic development and risk assessment. Therefore, this review highlights the importance of unlocking the potential of CP assays when combined with molecular fingerprints for compound evaluation and discusses the current challenges that are associated with this approach.
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Affiliation(s)
- Floriane Odje
- Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | - David Meijer
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
| | - Elena von Coburg
- Department Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany
| | | | - Sebastian Dunst
- Department Experimental Toxicology and ZEBET, German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), Berlin, Germany
| | - Marnix H. Medema
- Bioinformatics Group, Wageningen University, Wageningen, Netherlands
| | - Andrea Volkamer
- Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken, Germany
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Sui S, Zhou N, Liu H, Watson P, Yang X. Recognizing high-priority disinfection byproducts based on experimental and predicted endocrine disrupting data: Virtual screening and in vitro study. CHEMOSPHERE 2024; 358:142239. [PMID: 38705414 DOI: 10.1016/j.chemosphere.2024.142239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 04/25/2024] [Accepted: 05/02/2024] [Indexed: 05/07/2024]
Abstract
So far, about 130 disinfection by-products (DBPs) and several DBPs-groups have had their potential endocrine-disrupting effects tested on some endocrine endpoints. However, it is still not clear which specific DBPs, DBPs-groups/subgroups may be the most toxic substances or groups/subgroups for any given endocrine endpoint. In this study, we attempt to address this issue. First, a list of relevant DBPs was updated, and 1187 DBPs belonging to 4 main-groups (aliphatic, aromatic, alicyclic, heterocyclic) and 84 subgroups were described. Then, the high-priority endocrine endpoints, DBPs-groups/subgroups, and specific DBPs were determined from 18 endpoints, 4 main-groups, 84 subgroups, and 1187 specific DBPs by a virtual-screening method. The results demonstrate that most of DBPs could not disturb the endocrine endpoints in question because the proportion of active compounds associated with the endocrine endpoints ranged from 0 (human thyroid receptor beta) to 32% (human transthyretin (hTTR)). All the endpoints with a proportion of active compounds greater than 10% belonged to the thyroid system, highlighting that the potential disrupting effects of DBPs on the thyroid system should be given more attention. The aromatic and alicyclic DBPs may have higher priority than that of aliphatic and heterocyclic DBPs by considering the activity rate and potential for disrupting effects. There were 2 (halophenols and estrogen DBPs), 12, and 24 subgroups that belonged to high, moderate, and low priority classes, respectively. For individual DBPs, there were 23 (2%), 193 (16%), and 971 (82%) DBPs belonging to the high, moderate, and low priority groups, respectively. Lastly, the hTTR binding affinity of 4 DBPs was determined by an in vitro assay and all the tested DBPs exhibited dose-dependent binding potency with hTTR, which was consistent with the predicted result. Thus, more efforts should be performed to reveal the potential endocrine disruption of those high research-priority main-groups, subgroups, and individual DBPs.
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Affiliation(s)
- Shuxin Sui
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Nan Zhou
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Huihui Liu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Peter Watson
- Los Alamos National Laboratory, Los Alamos, 87545, New Mexico, United States
| | - Xianhai Yang
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
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4
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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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Wohlleben W, Mehling A, Landsiedel R. Lessons Learned from the Grouping of Chemicals to Assess Risks to Human Health. Angew Chem Int Ed Engl 2023; 62:e202210651. [PMID: 36254879 DOI: 10.1002/anie.202210651] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/15/2022] [Accepted: 10/17/2022] [Indexed: 11/05/2022]
Abstract
In analogy to the periodic system that groups elements by their similarity in structure and chemical properties, the hazard of chemicals can be assessed in groups having similar structures and similar toxicological properties. Here we review case studies of chemical grouping strategies that supported the assessment of hazard, exposure, and risk to human health. By the EU-REACH and the US-TSCA New Chemicals Program, structural similarity is commonly used as the basis for grouping, but that criterion is not always adequate and sufficient. Based on the lessons learned, we derive ten principles for grouping, including: transparency of the purpose, criteria, and boundaries of the group; adequacy of methods used to justify the group; and inclusion or exclusion of substances in the group by toxicological properties. These principles apply to initial grouping to prioritize further actions as well as to definitive grouping to generate data for risk assessment. Both can expedite effective risk management.
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Affiliation(s)
- Wendel Wohlleben
- Department of Analytical and Material Science, BASF SE, 67056, Ludwigshafen am Rhein, Germany
- Department of Experimental Toxicology and Ecology, BASF SE, 67056, Ludwigshafen am Rhein, Germany
| | - Annette Mehling
- Dept. of Advanced Formulation and Performance Technology, BASF Personal Care and Nutrition GmbH, 40589, Duesseldorf, Germany
| | - Robert Landsiedel
- Department of Experimental Toxicology and Ecology, BASF SE, 67056, Ludwigshafen am Rhein, Germany
- Free University of Berlin, Biology, Chemistry and Pharmacy-Pharmacology and Toxicology, 14195, Berlin, Germany
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An S, Hwang SY, Gong J, Ahn S, Park IG, Oh S, Chin YW, Noh M. Computational Prediction of the Phenotypic Effect of Flavonoids on Adiponectin Biosynthesis. J Chem Inf Model 2023; 63:856-869. [PMID: 36716271 DOI: 10.1021/acs.jcim.3c00033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In silico machine learning applications for phenotype-based screening have primarily been limited due to the lack of machine-readable data related to disease phenotypes. Adiponectin, a nuclear receptor (NR)-regulated adipocytokine, is relatively downregulated in human metabolic diseases. Here, we present a machine-learning model to predict the adiponectin-secretion-promoting activity of flavonoid-associated phytochemicals (FAPs). We modeled a structure-activity relationship between the chemical similarity of FAPs and their bioactivities using a random forest-based classifier, which provided the NR activity of each FAP as a probability. To link the classifier-predicted NR activity to the phenotype, we next designed a single-cell transcriptomics-based multiple linear regression model to generate the relative adiponectin score (RAS) of FAPs. In experimental validation, estimated RAS values of FAPs isolated from Scutellaria baicalensis exhibited a significant correlation with their adiponectin-secretion-promoting activity. The combined cheminformatics and bioinformatics approach enables the computational reconstruction of phenotype-based screening systems.
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Affiliation(s)
- Seungchan An
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Seok Young Hwang
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Junpyo Gong
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Sungjin Ahn
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - In Guk Park
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Soyeon Oh
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Young-Won Chin
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
| | - Minsoo Noh
- Natural Products Research Institute, College of Pharmacy, Seoul National University, Seoul08826, Republic of Korea
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Wu L, Yan B, Han J, Li R, Xiao J, He S, Bo X. TOXRIC: a comprehensive database of toxicological data and benchmarks. Nucleic Acids Res 2022; 51:D1432-D1445. [PMID: 36400569 PMCID: PMC9825425 DOI: 10.1093/nar/gkac1074] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 10/10/2022] [Accepted: 10/26/2022] [Indexed: 11/20/2022] Open
Abstract
The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.
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Affiliation(s)
| | | | - Junshan Han
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Ruijiang Li
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Jian Xiao
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China,Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China
| | - Song He
- Correspondence may also be addressed to Song He. Tel: +86 01066931450;
| | - Xiaochen Bo
- To whom correspondence should be addressed. Tel: +86 01066931207; ;
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Abstract
There is a need for paradigm change in the methodology employed for toxicological testing and assessment. It could be said that this change is well on its way, through an evolutionary progress analogous to that of natural selection. Darwin's Theory of Evolution has defined the idea of evolution and descendancy since the last third of the 19th century. Increasingly, this concept of 'evolution' is being applied beyond the field of biology. This Comment article discusses the progress of toxicological testing in the context of 'evolutionary pressure' and deliberates how this process can help foster the development, implementation and acceptance of mechanistic and human-relevant methods in this field. By comparing the current regulatory landscape in toxicity testing and assessment to specific elements in Charles Darwin's evolutionary theory, we aim to better understand the needs and requirements for the future.
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Affiliation(s)
- Robert Landsiedel
- 5184BASF SE, Experimental Toxicology and Ecology, Ludwigshafen am Rhein, Germany
- Free University of Berlin, Pharmacology and Toxicology, Berlin, Germany
| | - Barbara Birk
- 5184BASF SE, Experimental Toxicology and Ecology, Ludwigshafen am Rhein, Germany
| | - Dorothee Funk-Weyer
- 5184BASF SE, Experimental Toxicology and Ecology, Ludwigshafen am Rhein, Germany
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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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True Grit: A Story of Perseverance Making Two out of Three the First Non-Animal Testing Strategy (Adopted as OECD Guideline No. 497). COSMETICS 2022. [DOI: 10.3390/cosmetics9010022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In the last two decades, great strides have been made in developing alternative methods to animal testing for regulatory and safety testing. In 2021, a breakthrough in regulatory testing was achieved in that the first test strategies employing non-animal test methods for skin sensitization have been accepted as OECD guideline 497, which falls under the mutual acceptance of data (MAD) by OECD member states. Achieving this goal was a story of hard work and perseverance of the many people involved. This review gives an overview of some of the many aspects and timelines this entailed—just from the perspective of one stakeholder. In the end, the true grit of all involved allowed us to achieve not only a way forward in using test strategies for skin sensitization, but also a new approach to address other complex toxicological effects without the use of animals in the future.
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Wilm A, Garcia de Lomana M, Stork C, Mathai N, Hirte S, Norinder U, Kühnl J, Kirchmair J. Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors. Pharmaceuticals (Basel) 2021; 14:ph14080790. [PMID: 34451887 PMCID: PMC8402010 DOI: 10.3390/ph14080790] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/03/2021] [Accepted: 08/06/2021] [Indexed: 02/06/2023] Open
Abstract
In recent years, a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model (“Skin Doctor CP:Bio”) obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.
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Affiliation(s)
- Anke Wilm
- Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany; (A.W.); (C.S.)
- HITeC e.V., 22527 Hamburg, Germany
| | - Marina Garcia de Lomana
- Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria; (M.G.d.L.); (S.H.)
| | - Conrad Stork
- Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany; (A.W.); (C.S.)
| | - Neann Mathai
- Computational Biology Unit (CBU), Department of Chemistry, University of Bergen, N-5020 Bergen, Norway;
| | - Steffen Hirte
- Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria; (M.G.d.L.); (S.H.)
| | - Ulf Norinder
- MTM Research Centre, School of Science and Technology, Örebro University, SE-70182 Örebro, Sweden;
- Department of Computer and Systems Sciences, Stockholm University, SE-16407 Kista, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, SE-75124 Uppsala, Sweden
| | - Jochen Kühnl
- Front End Innovation, Beiersdorf AG, 22529 Hamburg, Germany;
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany; (A.W.); (C.S.)
- Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria; (M.G.d.L.); (S.H.)
- Correspondence: ; Tel.: +43-1-4277-55104
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