Chat GPT is a state-of-the-art language model that is developed by OpenAI. It is a deep neural network that has been trained on a large corpus of text, which enables it to generate humanlike responses to a wide range of prompts. Chat GPT utilizes the transformer architecture, which allows it to handle long-term dependencies and context in its language generation. It can be finetuned to specific tasks such as language translation, question answering and text completion, and can generate human-like text for various applications. Chat GPT is a notable example of the advancement in natural language processing and machine learning. Since, it has been trained by up till 2021, it processes the user questions and try to give us good information back on that topic. Hence the information provided by Chat GPT is not novel it will just provide the information uptill 2021. Drug discovery is the process of identifying and developing new medications to treat disease. It is a complex, multi-disciplinary field that involves various steps such as target identification, lead discovery, preclinical development, clinical trials, and regulatory approval. The process typically starts with the identification of a biological target that is involved in a disease process. Researchers then use various techniques such as high-throughput screening, computational chemistry, and structure-based drug design to identify small molecules or biologics that can interact with the target and modulate its activity. These lead compounds are then subjected to preclinical testing in various animal models and cell lines to evaluate their efficacy and safety before moving on to clinical trials in humans. The goal of drug discovery is to identify safe and effective treatments for a wide range of diseases. Computational based drug discovery process if shown in Figure . Drug discovery using computational chemistry is the application of computer-based methods to assist in the discovery of new medications. It involves the use of computer simulations and modeling techniques to predict the properties and interactions of potential drug molecules. These methods can be used to understand the structure and behavior of proteins and other biological targets, and to design and optimize new compounds that can bind to these targets and modulate their activity. Computational chemistry plays a vital role in modern drug discovery by providing a cost-effective and efficient way to screen large numbers of compounds, predict their potential efficacy and toxicity, and identify new lead compounds for further development. It also enables researchers to design new drugs that are more selective and have fewer side effects, as well as to understand the mechanisms of drug action at the molecular level, and to optimize the pharmacokinetics and pharmacodynamics of drugs. Drug discovery is a very difficult and complex process for several reasons: (1) Biological complexity: The human body is an incredibly complex system, and understanding how it works at the molecular level is difficult. Diseases are caused by a variety of factors, and finding a single drug that can treat a disease effectively is challenging; (2) High failure rate: The majority of drugs that enter clinical trials fail to be approved. This is because they are found to be ineffective, or they have unacceptable side effects during testing; (3) Time-consuming and expensive: The drug discovery process is long and costly. It can take up to 15 years and cost billions of dollars to bring a new drug to market; (4) Stringent regulatory requirements: The process of drug development is heavily regulated, and drugs must go through a series of preclinical and clinical trials before they can be approved for use; (5) Unpredictable nature of drug development: Even after a drug has been identified and developed, it's not certain that it will work as expected in human trials, sometimes it can have unexpected side effects; (6) Lack of new targets: Many of the known drug targets have already been exploited, and finding new targets is becoming increasingly difficult. Despite these challenges, researchers continue to work towards developing new and effective drugs. Advances in technology and computational chemistry, as well as a better understanding of disease biology, are helping to make the drug discovery process more efficient and cost-effective. In the current work, we have explored the usage and limitation of Chat GPT in drug discoveries.