Scholarlink

<p>Accurate prediction of aqueous solubility remains a critical challenge in the chemical and pharmaceutical industries, significantly influencing drug development and delivery. This study revisits this well-explored area by leveraging the advanced capabilities of modern computational resources. We apply an automated network optimizer model that integrates dual optimization processes for molecular features and hyperparameters, streamlining the traditionally complex hyperparameter search while providing an efficient interpretation of molecular properties. By employing feature optimization techniques, our deep neural network model demonstrates improvements in both the speed and accuracy of molecular property predictions, achieving an average performance of R<sup>2</sup> = 0.991. This result outperforms conventional hyperparameter optimization methods such as grid search and random search in predicting the intrinsic solubility of 3,745 compounds across four external experimental datasets. Based on feature importance analysis, we identified key molecular features and structures that significantly influence solubility. Additionally, combining three molecular fingerprints (Morgan, MACCS key, and Avalon) with molecular descriptors enhances model performance, providing a deeper understanding of the relationship between molecular structure and solubility within the physicochemical feature optimization process. These findings underscore the potential of machine learning models to improve predictive modeling of physical properties, apply automated modeling and feature selection to new chemical datasets, and offer explainable insights into the principles driving solubility predictions.</p><p><b>Scientific contributions</b></p><p>This article focuses on recent advancements in the prediction of molecular properties through the application of a Quantitative Structure-Property Relationship (QSPR)-based deep neural network (DNN) model. This model employs a dual optimization approach that integrates both molecular feature selection and hyperparameter tuning. A review of publicly available datasets for drug-sized molecules is presented, highlighting the contributions of automated modeling and feature selection in enhancing the predictive accuracy of physical properties. Additionally, the article addresses the efficacy of these machine learning models in optimizing features, an essential consideration for practical applications in the field.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prediction of intrinsic solubility for drug-like organic compounds using automated network optimizer (ANO) for physicochemical feature and hyperparameter optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月9日

查看全文
  • You Kyoung Chung,
  • Seung Jin Lee,
  • Junho Lee,
  • Himchanvit Cho,
  • Sung-Jin Kim,
  • Joonsuk Huh

摘要

Accurate prediction of aqueous solubility remains a critical challenge in the chemical and pharmaceutical industries, significantly influencing drug development and delivery. This study revisits this well-explored area by leveraging the advanced capabilities of modern computational resources. We apply an automated network optimizer model that integrates dual optimization processes for molecular features and hyperparameters, streamlining the traditionally complex hyperparameter search while providing an efficient interpretation of molecular properties. By employing feature optimization techniques, our deep neural network model demonstrates improvements in both the speed and accuracy of molecular property predictions, achieving an average performance of R2 = 0.991. This result outperforms conventional hyperparameter optimization methods such as grid search and random search in predicting the intrinsic solubility of 3,745 compounds across four external experimental datasets. Based on feature importance analysis, we identified key molecular features and structures that significantly influence solubility. Additionally, combining three molecular fingerprints (Morgan, MACCS key, and Avalon) with molecular descriptors enhances model performance, providing a deeper understanding of the relationship between molecular structure and solubility within the physicochemical feature optimization process. These findings underscore the potential of machine learning models to improve predictive modeling of physical properties, apply automated modeling and feature selection to new chemical datasets, and offer explainable insights into the principles driving solubility predictions.

Scientific contributions

This article focuses on recent advancements in the prediction of molecular properties through the application of a Quantitative Structure-Property Relationship (QSPR)-based deep neural network (DNN) model. This model employs a dual optimization approach that integrates both molecular feature selection and hyperparameter tuning. A review of publicly available datasets for drug-sized molecules is presented, highlighting the contributions of automated modeling and feature selection in enhancing the predictive accuracy of physical properties. Additionally, the article addresses the efficacy of these machine learning models in optimizing features, an essential consideration for practical applications in the field.