Applications of machine learning on compound property prediction
Compounds property prediction is an essential task in drug development,toxicology,and environmental behavior prediction.Along with an increasing number of synthetic chemicals,the corresponding experimental research data are expanding.However,the experimental data are still far away from rapid invention of novel chemicals.In recent years,machine learning algorithms and models have shown advantages and great potential in compound property prediction,especially in case of lacking experimental data,providing reliable model-predicted data.Our study outlines the main procedures and corresponding modules related to applications of machine learning tools for compound property prediction,specifically including datasets,molecular description methods,model performance evaluation metrics,and methods.Furthermore,this work systematically summarizes progress and advances in compound property prediction based on machine learning approaches,and also introduces specific examples on compounds predictions of physical and chemical properties,bioactivity,and toxicity.To end,the existing problems and challenges are discussed based on data sets,molecular characterization,and model outcome interpretation.
万方数据
维普
