Drug Like

"Drug-likeness" research focuses on computationally designing and predicting the properties of molecules suitable for drug development, aiming to accelerate and improve the drug discovery process. Current research heavily utilizes machine learning, particularly generative models like Generative Flow Networks (GFlowNets) and diffusion models, along with graph neural networks and transformers, to generate novel molecules with desired properties such as binding affinity, solubility, and synthetic accessibility. This field significantly impacts drug discovery by enabling the efficient exploration of vast chemical spaces, leading to the identification of promising drug candidates and potentially reducing the time and cost associated with traditional drug development.