Quantitative structure-toxicity relationship of chlorinated compounds: A molecular connectivity investigation

1983 ◽  
Vol 30 (1) ◽  
pp. 80-83 ◽  
Author(s):  
Aleksandar Sabljic
Keyword(s):  
Quantitative Structure ◽  
Molecular Connectivity ◽  
Chlorinated Compounds ◽  
Structure Toxicity Relationship ◽  
Relationship Of ◽  
Toxicity Relationship

scholarly journals Machine learning-based prediction of toxicity of organic compounds towards fathead minnow

RSC Advances ◽  
2020 ◽  
Vol 10 (59) ◽  
pp. 36174-36180 ◽  
Author(s):  
Xingmei Chen ◽  
Limin Dang ◽  
Hai Yang ◽  
Xianwei Huang ◽  
Xinliang Yu
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Organic Compounds ◽  
Fathead Minnow ◽  
Support Vector ◽  
Quantitative Structure ◽  
Structure Toxicity Relationship ◽  
Relationship Of ◽  
Toxicity Relationship

A quantitative structure–toxicity relationship of 963 chemicals against fathead minnow was developed by using support vector machine and genetic algorithm.

scholarly journals HUBUNGAN KUANTITATIF STRUKTUR TOKSISITAS : REVIEW

2020 ◽  
Vol 3 (2) ◽  
pp. 107-126
Author(s):  
Purwaniati Purwaniati
Keyword(s):  
In Silico ◽  
Toxicity Data ◽  
Quantitative Structure ◽  
Training Set ◽  
Drug Discovery And Development ◽  
Independent Variables ◽  
Drug Molecules ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship ◽  
In Silico Methods

AbstrakProses penemuan dan pengembangan obat merupakan proses panjang yang memerlukan banyak waktu dan biaya. Ada banyak calon molekul obat yang gagal mencapai pasaran karena alasan toksisitasnya yang tinggi, sehingga harus dapat diidentifikasi sedini mungkin. Hubungan kuantitatif struktur toksisitas (HKST) merupakan salah satu metode in silico yang cukup tangguh untuk memprediksi toksisitas. HKST merupakan persamaan matematis yang dibentuk dari variabel data endpoint toksisitas seperti LD50 sebagai variabel terikat dan sejumlah deskriptor sebagai variable bebas yang dihitung dari senyawa-senyawa dalam training set. Persamaan HKST kemudian digunakan untuk memprediksi toksisitas senyawa baru.Kata kunci : toksisitas, hubungan kuantitatif struktur toksisitas (HKST)AbstractThe process of drug discovery and development is a long process that requires a lot of time and costly. There are many prospective drug molecules that fail to reach the market due to high toxicity reasons, so they must be identified as early as possible. The quantitative structure toxicity relationship  (QSTR) is one of the in silico methods that is strong enough to predict toxicity. QSTR is a mathematical equation formed from endpoint toxicity data variables such as LD50 as a bound variable and a number of descriptors as independent variables calculated from the compounds in the training set. The QSTR equation is then used to predict the toxicity of new compounds.Keywords: toxicity, quantitative structure toxicity relationship (QSTR)

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