[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117983-en":3,"doc-seo-117983-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117983,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Prediction of Carboxylic Acid Toxicity Using Machine Learning Model","Carboxylic acids are organic compounds containing a carboxyl functional group that can donate a proton and form carboxylate ions in aqueous solutions. Due to the growing use and diversity of carboxylic acids, toxicity prediction is increasingly needed. This work builds predictive toxicity models using five molecular descriptors—refractive index, log P, pKa, density, and dipole moment—combined with machine learning algorithms. Performance is evaluated with decision tree, random forest, and k-NN, identifying decision tree as the most accurate for toxicity prediction.","MALAYSIAN JOURNAL OF APPLIED SCIENCES 2023 , VOL 8 (2): 28-36  \nE-ISSN:0127-9246 (ONLINE)  \n[http://dx.doi.org/10.37231/myjas.2023.8.2.357](http://dx.doi.org/10.37231/myjas.2023.8.2.357)  \n[https://journal.unisza.edu.my/myjas](https://journal.unisza.edu.my/myjas)  \nORIGINAL ARTICLE  \nPrediction of Carboxylic Acid Toxicity Using Machine Learning Model  \nZubainun Mohamed Zabidi1*, Nurul Batrisyia Muhamad Suhaimy1, Nur Diyana Nazihah Fuadi1, Nur Hanisah Hamzi1, Ahmad Nazib Alias 1  \nFaculty of Applied Sciences, Universiti Teknologi MARA, Cawangan Perak, Kampus Tapah, 35400 Tapah  \nRoad Perak  \n*Corresponding author: [zubainun384@uitm.edu.my](zubainun384@uitm.edu.my)  \nReceived: 17/05/2023, Accepted: 30/10/2023, Available Online: 31/10/2023  \nAbstract  \nCarboxylic acids are organic compounds characterized by the presence of a carboxyl functional group capable of donating a proton and forming carboxylate ions in aqueous solutions. The carboxylic acid has widely been used in in manufacturing and medical applications. The rapid growth in carboxylic acid has established a need to predict its toxicity. The purpose of this paper to build predictive toxicity of carboxylic acid models by using five molecular descriptors (refractive index, The octanol/water partition coefficient (log P), acid dissociation constant (pKa), density, and dipole moment) through Machine Learning algorithms. The accuracy of the Machine Learning algorithm was determine by using three different types of models which are Decision Tree, Random Forest and k-Nearest Neighbour (k-NN) . Among the machine learning algorithms used, we have determined that the decision tree is the best model for predicting the toxicity of carboxylic acid. This finding demonstrates that the decision tree model exhibits an acceptable level of performance in predicting toxicity within the field of toxicology.  \nKeywords: carboxylic acid; toxicity; machine learning  \nIntroduction  \nCarboxylic acids are organic compounds characterized by the presence of a carboxyl functional group (-COOH) in their chemical structure. The carboxyl group consists of a carbonyl group (C=O) and a hydroxyl group (OH) bonded to the same carbon atom. Carboxylic acids can be either aliphatic or aromatic, depending on the nature of the carbon chain attached to the carboxyl group. They are typically acidic in nature, capable of donating a proton and forming carboxylate ions in aqueous solutions. Carboxylic acids are widely used for manufacturing plastic, cosmetics, and medicine. For instance, carboxylic acid is applied in manufacturing plasticisers and resins, raw materials of polyester, and the synthesis of nylon(Bhadra, Ahmed, Lee, & Jhung, 2022) . Besides, in the medical field, ascorbic acid helps to maintain healthy skin, blood vessels, bone, and  \ncartilage, while benzoic acid acts as an antiseptic to treat urinary tract infections (Horgan & O’Sullivan, 2022) . Furthermore , benzoic acid is also used in facial cleansers(Bayo et al. , 2017) .  \nCarboxylic acids can present various hazards depending on their specific chemical properties, concentration, and exposure conditions (Jos et al. , 2009) . Hazards associated with carboxylic acids include corrosive effects on skin, eyes, and mucous membranes due to their acidic nature(Jiang et al. , 2019) . Carboxylic acids can exhibit varying degrees of drug toxicity depending on factors such as their chemical structure, concentration, and mode of interaction with biological systems (Mitra, 2022) . While some carboxylic acids are well-tolerated and even utilized therapeutically, others may pose toxicity concerns. The toxicity of carboxylic acids can arise from their ability to disrupt cellular processes, interfere with enzyme activity, or cause adverse effects on organs and tissues. The median lethal dose (LD50), used in toxicology, measures the amount of toxin, pathogen, or radiation needed to kill 50% of the test population within the text window. Determination of toxicity mi","cbCairpsKqan74Nj","https://ap.wps.com/l/cbCairpsKqan74Nj","pdf",704611,1,9,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What molecular descriptors are used to predict carboxylic acid toxicity?\",\"answer\":\"The study uses five descriptors: refractive index, the octanol/water partition coefficient (log P), acid dissociation constant (pKa), density, and dipole moment.\"},{\"question\":\"Which machine learning algorithms are compared in the toxicity prediction?\",\"answer\":\"Three models are evaluated: Decision Tree, Random Forest, and k-Nearest Neighbour (k-NN).\"},{\"question\":\"Why is the study interested in predicting carboxylic acid toxicity instead of relying only on animal or in vitro tests?\",\"answer\":\"The paper highlights that animal testing can be costly, slow, and raises ethics concerns, while in silico and machine learning approaches enable faster and more accurate toxicity screening for further experiments.\"}]","Prediction of Carboxylic Acid Toxicity Using Machine Learning Model | 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molecular descriptors are used to predict carboxylic acid toxicity?","Question",{"text":75,"@type":76},"The study uses five descriptors: refractive index, the octanol/water partition coefficient (log P), acid dissociation constant (pKa), density, and dipole moment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared in the toxicity prediction?",{"text":80,"@type":76},"Three models are evaluated: Decision Tree, Random Forest, and k-Nearest Neighbour (k-NN).",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the study interested in predicting carboxylic acid toxicity instead of relying only on animal or in vitro tests?",{"text":84,"@type":76},"The paper highlights that animal testing can be costly, slow, and raises ethics concerns, while in silico and machine learning approaches enable faster and more accurate toxicity screening for further 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