Flow-Based Fragment Identification via Contrastive Learning of Binding Site-Specific Latent Representations

Rebecca Manuela Neeser, Ilia Igashov, Arne Schneuing, Michael M. Bronstein, Philippe Schwaller, Bruno Correia

Submission Track: Full Paper

Submission Category: AI-Guided Design

Keywords: Representation Learning, Fragment-Based Drug Design, Generative Modeling

Abstract:

Fragment-based drug design is a promising strategy leveraging the binding of individual fragments, potentially yielding ligands with multiple key interactions, surpassing the efficiency of full ligand screening. The initial step of fragment identification remains challenging, as fragments often bind weakly and non-specifically. We propose a protein-fragment encoder, a new contrastive learning approach that
captures protein-fragment interactions. Its latent space allows to perform virtual
screening as well as generative design. In the latter case, fragment embeddings
are generated conditioned on the protein surface. Our method locates protein-fragment interactions with high sensitivity and can be directly applied to virtual screening for which we observed competitive fragment recovery rates. The
generative method outperforms common methods such as virtual screening providing a valuable starting point for fragment hit discovery. Together, these approaches contribute to advancing fragment identification and could provide valuable tools for fragment-based drug discovery. All the code and data can be found on https://github.com/rneeser/LatentFrag.

AI4Mat Journal Track: Yes

Submission Number: 36