Target-aware Variational Auto-encoders for Ligand Generation with
  Multimodal Protein Representation Learning
Nhat Khang Ngo Truong Son Hy
Without knowledge of specific pockets, generating ligands based on the global
structure of a protein target plays a crucial role in drug discovery as it
helps reduce the search space for potential drug-like candidates in the
pipeline. However, contemporary methods require optimizing tailored networks
for each protein, which is arduous and costly. To address this issue, we
introduce TargetVAE, a target-aware variational auto-encoder that generates
ligands with high binding affinities to arbitrary protein targets, guided by a
novel multimodal deep neural network built based on graph Transformers as the
prior for the generative model. This is the first effort to unify different
representations of proteins (e.g., sequence of amino-acids, 3D structure) into
a single model that we name as Protein Multimodal Network (PMN). Our multimodal
architecture learns from the entire protein structures and is able to capture
their sequential, topological and geometrical information. We showcase the
superiority of our approach by conducting extensive experiments and
evaluations, including the assessment of generative model quality, ligand
generation for unseen targets, docking score computation, and binding affinity
prediction. Empirical results demonstrate the promising performance of our
proposed approach. Our software package is publicly available at
https://github.com/HySonLab/Ligand_Generation