The client faced several issues regarding drug toxicity. The process the client used was time consuming and required clinical human trials to take place before a drug could be approved for production. First, drug discovery, which is a process to discover new candidate drugs, took place. After drug discovery was completed, the search for volunteers for human trials could be conducted, animal trials took place proving to be very crucial. Once a sufficient number of volunteers had enrolled, the clinical trials were intiated. The complete trial was conducted in several phases making the process lengthy and costly. It was only during the clinical trials that the drugs exhibited certain levels of toxicity which could be considered as fatal and have adverse effects on health. To start off the process, domain knowledge regarding drug discovery and drug toxicity was needed. A team of dedicated individuals was made to take up the task. Once the team had completed their study, research was conducted in order to find out which drugs failed their clinical trials for having high levels of toxicity. A list of all such drugs was compiled and their chemical and target properties were studied and jotted down. The trends of the chemical and target properties of failed drugs were studied and then compared with the chemical and target properties of drugs that had passed the clinical trials. The comparison helped figure out which features contributed to a drug being toxic or not. A Machine Learning (ML) model was trained on the shortlisted features and the model achieved an accuracy of 88% in the prediction of drugs having high levels of toxicity.