{"title":"机器学习加速从天然产品中筛选破骨细胞分化抑制剂","authors":"Yuki Hitora, Mako Hokaguchi, Yusaku Sadahiro, Takumi Higaki, Sachiko Tsukamoto","doi":"10.1021/acs.jnatprod.4c00640","DOIUrl":null,"url":null,"abstract":"<p><p>Natural products that inhibit osteoclast differentiation are promising therapeutic and preventive agents for osteoporosis. Conventionally, identifying osteoclast differentiation involves visual inspection of the microscope images of stained osteoclasts. In this study, a supervised machine learning model was developed to classify bright-field microscope images of osteoclasts without staining. The model was used to screen a compound library, and osteoclast differentiation inhibitors were identified, demonstrating the validity of our method. Next, an in-house library of fungal extracts was screened, and pinolidoxin was revealed as an inhibitor of osteoclast differentiation. Our machine learning method enabled accurate, objective, and high-throughput evaluation of osteoclast differentiation and efficient screening of the inhibitors from natural product extracts. This study represents the first machine learning classification developed to evaluate the inhibitory activity of natural products in osteoclast differentiation.</p>","PeriodicalId":47,"journal":{"name":"Journal of Natural Products ","volume":" ","pages":"2393-2397"},"PeriodicalIF":3.3000,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Accelerates Screening of Osteoclast Differentiation Inhibitors from Natural Products.\",\"authors\":\"Yuki Hitora, Mako Hokaguchi, Yusaku Sadahiro, Takumi Higaki, Sachiko Tsukamoto\",\"doi\":\"10.1021/acs.jnatprod.4c00640\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Natural products that inhibit osteoclast differentiation are promising therapeutic and preventive agents for osteoporosis. Conventionally, identifying osteoclast differentiation involves visual inspection of the microscope images of stained osteoclasts. In this study, a supervised machine learning model was developed to classify bright-field microscope images of osteoclasts without staining. The model was used to screen a compound library, and osteoclast differentiation inhibitors were identified, demonstrating the validity of our method. Next, an in-house library of fungal extracts was screened, and pinolidoxin was revealed as an inhibitor of osteoclast differentiation. Our machine learning method enabled accurate, objective, and high-throughput evaluation of osteoclast differentiation and efficient screening of the inhibitors from natural product extracts. This study represents the first machine learning classification developed to evaluate the inhibitory activity of natural products in osteoclast differentiation.</p>\",\"PeriodicalId\":47,\"journal\":{\"name\":\"Journal of Natural Products \",\"volume\":\" \",\"pages\":\"2393-2397\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Natural Products \",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.jnatprod.4c00640\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/4 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Natural Products ","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1021/acs.jnatprod.4c00640","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/4 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
Machine Learning Accelerates Screening of Osteoclast Differentiation Inhibitors from Natural Products.
Natural products that inhibit osteoclast differentiation are promising therapeutic and preventive agents for osteoporosis. Conventionally, identifying osteoclast differentiation involves visual inspection of the microscope images of stained osteoclasts. In this study, a supervised machine learning model was developed to classify bright-field microscope images of osteoclasts without staining. The model was used to screen a compound library, and osteoclast differentiation inhibitors were identified, demonstrating the validity of our method. Next, an in-house library of fungal extracts was screened, and pinolidoxin was revealed as an inhibitor of osteoclast differentiation. Our machine learning method enabled accurate, objective, and high-throughput evaluation of osteoclast differentiation and efficient screening of the inhibitors from natural product extracts. This study represents the first machine learning classification developed to evaluate the inhibitory activity of natural products in osteoclast differentiation.
期刊介绍:
The Journal of Natural Products invites and publishes papers that make substantial and scholarly contributions to the area of natural products research. Contributions may relate to the chemistry and/or biochemistry of naturally occurring compounds or the biology of living systems from which they are obtained.
Specifically, there may be articles that describe secondary metabolites of microorganisms, including antibiotics and mycotoxins; physiologically active compounds from terrestrial and marine plants and animals; biochemical studies, including biosynthesis and microbiological transformations; fermentation and plant tissue culture; the isolation, structure elucidation, and chemical synthesis of novel compounds from nature; and the pharmacology of compounds of natural origin.
When new compounds are reported, manuscripts describing their biological activity are much preferred.
Specifically, there may be articles that describe secondary metabolites of microorganisms, including antibiotics and mycotoxins; physiologically active compounds from terrestrial and marine plants and animals; biochemical studies, including biosynthesis and microbiological transformations; fermentation and plant tissue culture; the isolation, structure elucidation, and chemical synthesis of novel compounds from nature; and the pharmacology of compounds of natural origin.