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ToxMPNN: A deep learning model for small molecule toxicity prediction.
Zhou, Yini; Ning, Chao; Tan, Yijun; Li, Yaqi; Wang, Jiaxu; Shu, Yuanyuan; Liang, Songping; Liu, Zhonghua; Wang, Ying.
Afiliación
  • Zhou Y; The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China.
  • Ning C; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China.
  • Tan Y; Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China.
  • Li Y; The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China.
  • Wang J; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China.
  • Shu Y; Peptide and Small Molecule Drug R&D Platform, Furong Laboratory, Hunan Normal University, Changsha, China.
  • Liang S; School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China.
  • Liu Z; The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, China.
  • Wang Y; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, China.
J Appl Toxicol ; 44(7): 953-964, 2024 Jul.
Article en En | MEDLINE | ID: mdl-38409892
ABSTRACT
Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is often limited. Because of the unsatisfactory performance of models trained on a single toxicity endpoint, we collected toxic small molecules with multiple toxicity endpoints from previous study. The dataset comprises 27 toxic endpoints categorized into seven toxicity classes, namely, carcinogenicity and mutagenicity, acute oral toxicity, respiratory toxicity, irritation and corrosion, cardiotoxicity, CYP450, and endocrine disruption. In addition, a binary classification Common-Toxicity task was added based on the aforementioned dataset. To improve the performance of the models, we added marketed drugs as negative samples. This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common-Toxicity task but also enhances the stability of the model prediction. It shows that the graph-based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Límite: Humans Idioma: En Revista: J Appl Toxicol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Límite: Humans Idioma: En Revista: J Appl Toxicol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido