Table 1.
List of models to predict toxicity through the structure of chemicals
| Models | Features |
|---|---|
| Deductive estimate of risk from existing knowledge (Derek) | Developed by Lhasa Ltd., Derek is a type of QSAR model based on professional experience rules. Derek is a knowledge-based toxicity prediction program that divides a series of categories based on chemical structure, predicts the correlation between structure and biological activity, and can predict various toxicity indicators, including genotoxicity. Toxicophore that causes toxicity in target chemicals is identified to predict toxicity [27]. At this time, the risk assessment is defined on the basis of the relevant literature, which ensures strong confidence in the prediction. Knowledge-based toxicity prediction programs are used to determine the potential toxicity of a substance by obtaining information on the toxic functional groups [4]. In particular, the company is conducting research to improve the predictive power by applying multiple prediction programs, such as Sarah and Toxtree, and multiple applications [28]. Because in silico systems predict more complex phenomena that can use limited data, maximizing data accessibility is becoming increasingly important. In particular, the company is conducting research to improve the predictive power by applying multiple prediction programs, such as Sarah and Toxtree, and multiple applications [29] |
| EPA toxicity estimation software tool (T.E.S.T.) | The Chemistry Development Kit Java open-source and chemical data interworking is characterized by validating results by applying eight characteristic QSAR methods. Input query uses chemical name, CAS No., structure text file, etc., and the range includes acute oral toxicity, gene mutations, and environmental toxicity. Software provided by the US EPA includes human rat LD50 developmental toxicity and genotoxicity models |
| DanishQSAR | This is a repository-based model with data of more than 600,000 chemicals, applicable to 200 QSAR models. Input query can be chemical name, structure, CAS No., SMILES form, Mol file, etc. The range of prediction is physicochemical characteristics, acute toxicity, skin corrosion/irritation, and environmental toxicity |
| VegaHubQSAR | This provides prediction results optimized for REACH requirements and holds 40,000 kinds of chemical data, enabling the simultaneous batch prediction of a large number of substances and supporting the read-across approach. The input query uses the SMILES form, and the prediction range is mutagenic, carcinogenic, skin sensitized, BCF, logP, etc. Vega is a model for predicting human toxicity, which includes models for mutagenicity, carcinogenicity, developmental toxicity, endocrine binding, and skin sensitization, and physicochemical property prediction models. Vega implements and provides models to ensure that the in silico method is used correctly and that professionals use the in silico model |
| Toxtree | Toxtree is the software that implements the decision tree (DT) proposed by Cramer. Cramer DT is classified into three classes according to the metabolism of the compound, information on toxicity data, and information on whether it is used as a component of traditional food. Class 1 substances are known for their metabolic information and are very toxic compounds, and substances such as alcohols, ketones, and aldehydes belong to the first class. Class 2 is intermediate and is more toxic than class 1, but class 2 substances do not exhibit the same toxicity as class 3. Substances belonging to class 2 fall into one of two categories, with functional groups similar to those of class 1, but with higher reactivity, or more complex structure, than class 1. Class 3 is a highly toxic structure that contains compounds with highly reactive functional groups. Cramer’s method consists of 33 questions, and the answer to each question is yes or no. The compounds are classified according to the answers to these questions |
| PreADMET | The PreADMET package provides carcinogenicity prediction models and genotoxicity prediction models. A carcinogenicity prediction model was developed using data from mice administered with a chemical for two years, to determine whether cancer developed. A genotoxicity model was developed using data from the Ames test |