Generative AI refers to a class of artificial intelligence models that are capable of creating new data samples resembling the original data they were trained on. These models learn the underlying patterns and distributions of the data, enabling them to generate novel instances with similar properties. Some popular generative AI techniques include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based language models.
In the context of drug discovery, generative AI has emerged as a powerful tool in recent years, offering a more efficient and effective approach to identifying and optimizing new drug candidates. By leveraging advanced techniques like GANs and VAEs, researchers can explore vast chemical spaces, predict molecular properties, and accelerate the drug development process. In this article, we'll delve into the use of generative models in drug discovery, providing code snippets to demonstrate their implementation.