Our “Smart Molecule Generation” system is an Artificial Intelligence (AI) based automatic system which can generate novel molecules by using the machine learning techniques. The system comprises a computer model in the background
which reads SMILES (Simplified Molecular Input Line Entry System), SMILES represents the structural information of the molecules into a string of symbols.
The SMILES were fed to the machine learning model where the model learns the inherent patterns of the SMILES and generates the molecules which were not present in the training as well testing data. The molecules were validated
on the basis of the structures. The novelty of the molecules were tested on SciFinder and it was found that the model has the Potentiality to generate structurally valid SMILES which were never reported earlier.
In line with smart molecule generation system, we provide In-silico toxicity evaluation. The function of this system is to test the toxicity of the molecules using earlier recorded results.
The data from different public and commercial domains were collected and models were built on the procured data. The toxicity prediction system encompass the machine learning models which provides the information about the
molecules in quantitative as well qualitative format.
The In-silico methods for toxicity prediction will reduce the overall time and cost behind the pre-clinical trials.
