ArticleMolecules (Basel, Switzerland)2020
Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library.
Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 29 citations in OpenAlex.
- DeepSnap: From Three-Dimensional Molecular Images to Quantitative Structure-Activity Predictions.International journal of molecular sciences · 2026Review
- M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy.BMC bioinformatics · 2025Article
- A Comprehensive Exploration of Bioluminescence Systems, Mechanisms, and Advanced Assays for Versatile Applications.Biochemistry research international · 2024Review
- Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening.ACS omega · 2023Article
- Knowledge graph aids comprehensive explanation of drug and chemical toxicity.CPT: pharmacometrics & systems pharmacology · 2023Article
- Ensemble Learning, Deep Learning-Based and Molecular Descriptor-Based Quantitative Structure-Activity Relationships.Molecules (Basel, Switzerland) · 2023Review
- Knowledge-guided deep learning models of drug toxicity improve interpretation.Patterns (New York, N.Y.) · 2022Article
- Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning.ACS omega · 2022Article
- A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance.International journal of molecular sciences · 2022Article
- Review ofFrontiers in endocrinology · 2022Review
- Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System.International journal of molecular sciences · 2021Article
- Article
- Prediction of Molecular Properties Using Molecular Topographic Map.Molecules (Basel, Switzerland) · 2021Article
- The Role of Androgens and Androgen Receptor in Human Bladder Cancer.Biomolecules · 2021Review
- Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The interaction of nuclear receptors (NRs) with chemical compounds can cause dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to the disruption of natural hormones. Thus, identifying possible ligands of NRs is a crucial task for understanding the adverse outcome pathway (AOP) for human toxicity as well as the development of novel drugs. However, the experimental assessment of novel ligands remains expensive and time-consuming. Therefore, an in silico approach with a wide range of applications instead of experimental examination is highly desirable. The recently developed novel molecular image-based deep learning (DL) method, DeepSnap-DL, can produce multiple snapshots from three-dimensional (3D) chemical structures and has achieved high performance in the prediction of chemicals for toxicological evaluation. In this study, we used DeepSnap-DL to construct prediction models of 35 agonist and antagonist allosteric modulators of NRs for chemicals derived from the Tox21 10K library. We demonstrate the high performance of DeepSnap-DL in constructing prediction models. These findings may aid in interpreting the key molecular events of toxicity and support the development of new fields of machine learning to identify environmental chemicals with the potential to interact with NR signaling pathways.
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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.