Toxicity Detection in Drug Candidates using Simplified Molecular-Input Line-Entry System

TL;DR

This paper explores using SMILES representations with LSTM models to predict chemical toxicity efficiently, aiming to enhance rapid drug candidate screening for safer pharmaceuticals.

Contribution

It introduces a novel approach combining SMILES and LSTM models for toxicity prediction, advancing AI-based drug safety assessment methods.

Findings

LSTM models effectively predict chemical toxicity from SMILES data.

The approach shows promise for practical, real-world drug screening applications.

Potential for faster, more accurate toxicity assessments in drug development.

Abstract

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 paper 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.