A Long short-term memory based classifier to classify new drug candidates if toxic using Simplified molecular-input line-entry system notation. The need for analysis of toxicity in new drug candidates and the requirement of doing it fast have asked the consideration of scientists towards the use of artificial intelligence tools to examine toxicity levels and to develop models to a degree where they can be used commercially to measure toxicity levels efficiently in upcoming drugs. Artificial Intelligence based models can be used to predict the toxic nature of a chemical using Quantitative Structure–Activity Relationship techniques. Convolutional Neural Network models have demonstrated great outcomes in predicting the qualitative analysis of chemicals in order to determine the toxicity. This project goes for the study of Simplified Molecular-Input Line-Entry System (SMILES) as a parameter to develop Long-short term memory (LSTM) based models in order to examine the toxicity of a molecule and the degree to which the need can be fulfilled for practical use alongside its future outlooks for the purpose of real world applications. Read the published paper at https://www.ijcaonline.org/archives/volume175/number21/31573-2020920695

@article{10.5120/ijca2020920695,
author = {Nath Mriganka and Goswami Subhasish},
title = {Toxicity Detection in Drug Candidates using Simplified Molecular-Input Line-Entry System},
journal = {International Journal of Computer Applications},
issue_date = {September 2020},
volume = {175},
number = {21},
month = {Sep},
year = {2020},
issn = {0975-8887},
pages = {1-4},
numpages = {4},
url = {http://www.ijcaonline.org/archives/volume175/number21/31573-2020920695},
doi = {10.5120/ijca2020920695},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}