Fig. 4. Design of new kinase inhibitors using a generative artificial intelligence model.

The variational autoencoder, trained with the structures of many compounds, can encode a molecular structure into a latent space of numerical vectors and decode this latent space back into the compound structure. For each target, such as the receptor tyrosine kinase DDR1, the variational autoencoder can create embeddings of compound categories, such as existing kinase inhibitors, patented compounds and non-kinase inhibitors. Sampling the latent space for compounds that are similar to existing on-target inhibitors and not patented compounds or non-kinase inhibitors can generate new candidate kinase inhibitors for downstream experimental validation. Adapted from ref.¹³⁶, Springer Nature Limited.