인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Water solubility is a relevant physico-chemcial property in environmental chemistry, toxicology, and drug design. Although the water solubility is besides the octanol-water partition coefficient, melting point, and boiling point a property with a large amount of available experimental data, there are still more compounds in the chemical universe for which information on their water solubility is lacking. Thus, prediction tools with a broad application domain are needed to fill the corresponding data gaps. To this end, we developed a graph convolutional neural network model (GNN) to predict the water solubility in the form of log S<sub>w</sub> based on a highly curated dataset of 9800 chemicals. We started our model development with a curation workflow of the AqSolDB data, ending with 7605 data points. We added 2195 chemicals with experimental data, which we found in the literature, to our dataset. In the final dataset, log S<sub>w</sub> values range from - 13.17 to 0.50. Higher values were excluded by a cut-off introduced to eliminate fully miscible chemicals. We developed a consensus GNN by a fivefold split of the corresponding training set (70% of the data) and validation set (20%) and used 10% as independent test set for the evaluation of the performance of the different splits and the consensus model. By doing so, we achieved an r<sup>2</sup> of 0.901, a q<sup>2</sup> of 0.896, and an rmse of 0.657 on our independently selected test set, which is close to the experimental error of 0.5 to 0.6 log units. We further provide the information on the application domain and compare our performance to other existing prediction tools.Scientific contribution Based on a highly curated dataset, we developed a neural network to predict the water solubility of chemicals for a broad application domain. Data curation was done by us in a step-wise procedure, where we identified various errors in the experimental data. Based on an independent test set, we compare our prediction results to those of the available prediction models.
#Solubility
#Computer science
#Test set
#Boiling point
#Data mining
#Convolutional neural network
#Workflow
#Data set
#Partition coefficient
#Set (abstract data type)
#Graph
#Artificial neural network
#Test data
#Machine learning
#Artificial intelligence
#Chemistry
#Database
#Theoretical computer science
#Chromatography
#Organic chemistry
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