Artificial intelligence (AI) is revolutionizing drug discovery by enhancing efficiency, reducing costs, and accelerating the development of new drugs. The traditional drug discovery process is time-consuming, expensive, and complex, involving multiple stages from target identification to clinical trials. AI, particularly through machine learning (ML) and deep learning (DL), is being increasingly used to address these challenges. ML algorithms can predict pharmacokinetic and toxicological properties of drug candidates, while DL techniques are used to analyze complex biological data and improve the accuracy of drug discovery processes. AI has been applied in various aspects of drug discovery, including target identification, compound screening, drug design, and toxicity assessment. The integration of AI with drug discovery offers a promising strategy to overcome the challenges and complexities of the pharmaceutical industry. AI-based technologies, such as virtual screening, molecular docking, and predictive modeling, are being used to identify potential drug candidates and optimize their properties. Additionally, AI is being used to predict the toxicity of drugs and assess their safety, which is crucial for the approval of new drugs. The application of AI in drug discovery is rapidly evolving, with new techniques and algorithms being developed to improve the accuracy and efficiency of the process. The use of AI in drug discovery is expected to significantly reduce the time and cost associated with traditional methods, leading to the development of more effective and safer drugs.Artificial intelligence (AI) is revolutionizing drug discovery by enhancing efficiency, reducing costs, and accelerating the development of new drugs. The traditional drug discovery process is time-consuming, expensive, and complex, involving multiple stages from target identification to clinical trials. AI, particularly through machine learning (ML) and deep learning (DL), is being increasingly used to address these challenges. ML algorithms can predict pharmacokinetic and toxicological properties of drug candidates, while DL techniques are used to analyze complex biological data and improve the accuracy of drug discovery processes. AI has been applied in various aspects of drug discovery, including target identification, compound screening, drug design, and toxicity assessment. The integration of AI with drug discovery offers a promising strategy to overcome the challenges and complexities of the pharmaceutical industry. AI-based technologies, such as virtual screening, molecular docking, and predictive modeling, are being used to identify potential drug candidates and optimize their properties. Additionally, AI is being used to predict the toxicity of drugs and assess their safety, which is crucial for the approval of new drugs. The application of AI in drug discovery is rapidly evolving, with new techniques and algorithms being developed to improve the accuracy and efficiency of the process. The use of AI in drug discovery is expected to significantly reduce the time and cost associated with traditional methods, leading to the development of more effective and safer drugs.