Generating Apoptosis-Inducing Anticancer Peptides Targeting BCL-xL Using Latent Diffusion Models on Small Datasets

Abstract

The overexpression of B-cell lymphoma-extra large (BCL-xL), an anti-apoptotic protein, plays a pivotal role in various cancers by inhibiting apoptosis and promoting tumor progression. We introduce Latent Diffusion Model (LDM) specifically designed to generate apoptosis-inducing Anticancer Peptides (ACPs) targeting BCL-xL. By applying a diffusion process to high-dimensional embeddings generated by ESM-2, a state-of-the-art Protein Language Model (PLM), our approach successfully generates peptides with optimal physicochemical properties, even when trained on a relatively small dataset. In comparison to Variational Autoencoder (VAE) and Wasserstein Autoencoder (WAE), our architecture not only mitigates the limitations associated with small datasets but also produces peptides that closely align with desired physiochemical properties. Evaluation against key metrics—molecular weight, isoelectric point, net charge at pH 7, grand average of hydropathy, instability index, and anticancer peptide score,demonstrates that our model consistently outperforms both VAE and WAE, offering a promising strategy for the development of novel anticancer therapies.