A graph neural network approach for molecule carcinogenicity prediction

Fradkin, Philip; Young, Adamo; Atanackovic, Lazar; Frey, Brendan J.; Lee, Leo J.; Wang, Bo

doi:10.1093/bioinformatics/btac266

Cited by 18 publications

(8 citation statements)

References 36 publications

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“…Recently, numerous studies have examined the intrinsic information and usefulness of different descriptors and fingerprints. Several researchers in the field have reached the consensus that any type, especially when employed in combination, can effectively accomplish the task, such as FP-GNN and CheMixNet. − The molecular descriptors/fingerprints and molecular graphs were combined as input features for the construction of a model architecture. Specifically, for the traditional models (SVM, XGB, and LGB), we concatenated the D-MPNN graph features with alvaDesc descriptors as input features.…”

Section: Resultsmentioning

confidence: 99%

Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation

Yang,

Wang,

Yang

et al. 2024

Environ. Sci. Technol.

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Over the past few decades, extensive research has indicated that exposure to bisphenol A (BPA) increases the health risks in humans. Toxicological studies have demonstrated that BPA can bind to the androgen receptor (AR), resulting in endocrinedisrupting effects. In recent investigations, many alternatives to BPA have been detected in various environmental media as major pollutants. However, related experimental evaluations of BPA alternatives have not been systematically implemented for the assessment of chemical safety and the effects of structural characteristics on the antagonistic activity of the AR. To promote the green development of BPA alternatives, high-throughput toxicological screening is fundamental for prioritizing chemical tests. Therefore, we proposed a hybrid deep learning architecture that combines molecular descriptors and molecular graphs to predict AR antagonistic activity. Compared to previous models, this hybrid architecture can extract substantial chemical information from various molecular representations to improve the model's generalization ability for BPA alternatives. Our predictions suggest that lignin-derivable bisguaiacols, as alternatives to BPA, are likely to be nonantagonist for AR compared to bisphenol analogues. Additionally, molecular dynamics (MD) simulations identified the dihydrotestosterone-bound pocket, rather than the surface, as the major binding site of bisphenol analogues. The conformational changes of key helix H12 from an agonistic to an antagonistic conformation can be evaluated qualitatively by accelerated MD simulations to explain the underlying mechanism. Overall, our computational study is helpful for toxicological screening of BPA alternatives and the design of environmentally friendly BPA alternatives.

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Section: Resultsmentioning

confidence: 99%

Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation

Yang,

Wang,

Yang

et al. 2024

Environ. Sci. Technol.

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“…In recent years, many AI-based models and tools have been developed to predict the carcinogenic potential of compounds. ,,,− Limbu and Dakshanamurthy built carcinogenicity prediction models using a hybrid NN architecture with three data sets containing more than 10,000 chemicals, and 653 molecular descriptors . The hybrid NN achieved an average ACC of 74.3% and mean ROC-AUC of 80.6%, which were superior to those of the AdaBoost model but not those of the Bagging and RF models using the same data.…”

Section: Recent Advances In Ai-based Drug Toxicity Predictionmentioning

confidence: 99%

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Tran

Wibowo

Tayara

et al. 2023

J. Chem. Inf. Model.

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Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

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“…Mutagenicity refers to a substance’s ability to cause genetic mutations, potentially leading to various disorders, including cancer, while carcinogenicity is a compound’s potential to cause cancer [ 212 , 213 , 214 ]. Given the correlation between these two and the global burden of cancer, it is vital to evaluate mutagenicity and carcinogenicity [ 215 , 216 , 217 ]. Challenges in this task include mutagenicity, inconsistencies in Ames test results, false positives and negatives, and reproducibility issues among labs [ 218 , 219 ].…”

Section: Ai In Medicinal Chemistry or Cheminformaticsmentioning

confidence: 99%

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

Han,

Yoon,

Kim

et al. 2023

Pharmaceuticals

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Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug–target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI’s expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI’s growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.

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A graph neural network approach for molecule carcinogenicity prediction

Cited by 18 publications

References 36 publications

Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation

Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

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