S-COPHY:基于单个三维分子图像预测化妆品或药品化合物化学类别的深度学习模型

IF 3.1 Q2 TOXICOLOGY
Tomoka Hisaki , Koki Yoshida , Takumi Nukaga , Shinya Iwanaga , Masaaki Mori , Yoshihiro Uesawa , Shuichi Sekine , Akiko Tamura
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引用次数: 0

摘要

用于化妆品成分安全性评估的非动物体外和硅学方法(最近被称为下一代风险评估 (NGRA) / 新方法 (NAM))正在迅速发展,成为监管部门接受新材料的依据。然而,预测模型只能应用于生成模型所用数据集所定义的化学空间内的化学品。因此,只有对与建模集相对相似的新分子的预测才能被认为是可靠的,具有很强的可信度。在本研究中,我们开发了 S-COPHY 模型,该模型采用深度学习方法,根据新化合物与大量医药和化妆品化合物的结构相似性对其进行分类。S-COPHY 在内部和外部都显示出很高的预测准确性,特别是只有少数情况下,药品被错误地预测为化妆品。深度学习的使用实现了根据 SMILES(简化分子输入行输入系统)信息自动生成输入数据,从而使模型结果更加一致。此外,GRAD-CAM(梯度加权类活化图)分析有助于深入了解有助于模型预测的特定结构。S-COPHY 能够识别与类药物活性相关的特征结构,这表明它在支持化妆品成分安全性评估方面具有潜在价值。我们的研究结果表明,S-COPHY 模型是一种很有前途的方法,可用于支持大型化学空间的决策,从而有助于化妆品成分的安全性评估。将该模型扩展到其他类别(如杀虫剂)可进一步扩大其适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
S-COPHY: A deep learning model for predicting the chemical class of compounds as cosmetics or pharmaceuticals based on single 3D molecular images

Non-animal-based in vitro and in silico approaches for the safety assessment of cosmetic ingredients, recently referred to as Next Generation Risk Assessment (NGRA)/New Approach Methodologies (NAMs), are evolving rapidly as approaches to provide a basis for the regulatory acceptance of new materials. However, predictive models should be applied only to chemicals within the chemical space defined by the dataset used in generating the model. Thus, only predictions for new molecules that are relatively similar to the modeling set can considered reliable with strong confidence. In this study, we developed the S-COPHY model, which employs deep learning to classify new compounds based on their structural similarity to a large collection of pharmaceutical and cosmetic compounds. S-COPHY shows high predictive accuracy both internally and externally, and in particular, there were only a few instances where pharmaceuticals were incorrectly predicted as cosmetics. The use of deep learning enabled the automatic generation of input data from SMILES (Simplified Molecular Input Line Entry System) information, resulting in more consistent model outcomes. Furthermore, GRAD-CAM (Gradient-weighted Class Activation Map) analysis provided insights into the specific structures that contribute to the model's predictions. The potentiality of S-COPHY to identify characteristic structures associated with pharmaceutical-like activity indicates its potential value in supporting safety assessments of cosmetic ingredients. Our results indicate that the S-COPHY model is a promising approach to support decision-making in large chemical spaces, thereby contributing to the safety evaluation of cosmetic ingredients. Expansion of the model to other categories, such as pesticides, could further extend its applicability.

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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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