Generative AI is being used in drug discovery to generate new molecular structures for drugs, design antibodies, and identify new targets for drug development. It is transforming the landscape of drug discovery by leveraging the principles of unsupervised learning to create molecular structures. Generative AI is being used in drug discovery in the following ways:

• Molecular structure generation: Speeding up the discovery process by creating new drug candidates with desired attributes.
• Antibody design enhancement: Facilitating the design of more effective antibodies for diseases like cancer.
• Identifying new targets: Identifying new targets for drug development.

Generative AI in drug discovery promises to revolutionize the drug discovery process by augmenting traditional methods with computational efficiency and accuracy. It can accelerate the drug discovery process by enabling researchers to explore a vast chemical space in a short amount of time, shortening the time between initial discovery and clinical trials. Generative AI in drug discovery holds the promise of accelerating drug development, reducing costs, and ultimately saving lives. However, there are challenges in creating large datasets and ensuring algorithm efficiency and accuracy.