ArticleJournal of computer-aided molecular design2020
Revealing cytotoxic substructures in molecules using deep learning.
Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Current and emerging new approach & methodologies for skin hazard assessment.Toxicological research · 2026Review
- Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features.Acta pharmaceutica Sinica. B · 2025Article
- Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2025Review
- Explainable Artificial Intelligence in the Field of Drug Research.Drug design, development and therapy · 2025Review
- Cyto-Safe: A Machine Learning Tool for Early Identification of Cytotoxic Compounds in Drug Discovery.Journal of chemical information and modeling · 2024Article
- Sort & Slice: a simple and superior alternative to hash-based folding for extended-connectivity fingerprints.Journal of cheminformatics · 2024Article
- Artificial intelligence for natural product drug discovery.Nature reviews. Drug discovery · 2023Review
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Automatic identification of chemical moieties.Physical chemistry chemical physics : PCCP · 2023Article
- Evaluating the utility of a high throughput thiol-containing fluorescent probe to screen for reactivity: A case study with the Tox21 library.Computational toxicology (Amsterdam, Netherlands) · 2023Article
- Discovery of 4-aminoindole carboxamide derivatives to curtail alpha-synuclein and tau isoform 2N4R oligomer formation.Results in chemistry · 2023Article
- Identification of Potential Insect Growth Inhibitor againstInternational journal of molecular sciences · 2022Article
- Accelerating antibiotic discovery through artificial intelligence.Communications biology · 2021Review
- NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism.eLife · 2021Article
- Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.PLoS computational biology · 2021Article
- Benchmarks for interpretation of QSAR models.Journal of cheminformatics · 2021Article
- Deep Learning in Virtual Screening: Recent Applications and Developments.International journal of molecular sciences · 2021Review
- Artificial intelligence in chemistry and drug design.Journal of computer-aided molecular design · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical advances and the ever growing amount of available toxicity data enabled machine learning, especially neural networks, to impact the field of predictive toxicology. In this study, cytotoxicity prediction, one of the earliest handles in drug discovery, is investigated using a deep learning approach trained on a highly consistent in-house data set of over 34,000 compounds with a share of less than 5% of cytotoxic molecules. The model reached a balanced accuracy of over 70%, similar to previously reported studies using Random Forest. Albeit yielding good results, neural networks are often described as a black box lacking deeper mechanistic understanding of the underlying model. To overcome this absence of interpretability, a Deep Taylor Decomposition method is investigated to identify substructures that may be responsible for the cytotoxic effects, the so-called toxicophores. Furthermore, this study introduces cytotoxicity maps which provide a visual structural interpretation of the relevance of these substructures. Using this approach could be helpful in drug development to predict the potential toxicity of a compound as well as to generate new insights into the toxic mechanism. Moreover, it could also help to de-risk and optimize compounds.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.