PepTune: De Novo Generation of Therapeutic Peptides with
  Multi-Objective-Guided Discrete Diffusion
Authors: name: Sophia Tang, name: Yinuo Zhang, name: Pranam Chatterjee

Peptide therapeutics, a major class of medicines, have achieved remarkable
success across diseases such as diabetes and cancer, with landmark examples
such as GLP-1 receptor agonists revolutionizing the treatment of type-2
diabetes and obesity. Despite their success, designing peptides that satisfy
multiple conflicting objectives, such as target binding affinity, solubility,
and membrane permeability, remains a major challenge. Classical drug
development and structure-based design are ineffective for such tasks, as they
fail to optimize global functional properties critical for therapeutic
efficacy. Existing generative frameworks are largely limited to continuous
spaces, unconditioned outputs, or single-objective guidance, making them
unsuitable for discrete sequence optimization across multiple properties. To
address this, we present PepTune, a multi-objective discrete diffusion model
for the simultaneous generation and optimization of therapeutic peptide SMILES.
Built on the Masked Discrete Language Model (MDLM) framework, PepTune ensures
valid peptide structures with state-dependent masking schedules and
penalty-based objectives. To guide the diffusion process, we propose a Monte
Carlo Tree Search (MCTS)-based strategy that balances exploration and
exploitation to iteratively refine Pareto-optimal sequences. MCTS integrates
classifier-based rewards with search-tree expansion, overcoming gradient
estimation challenges and data sparsity inherent to discrete spaces. Using
PepTune, we generate diverse, chemically-modified peptides optimized for
multiple therapeutic properties, including target binding affinity, membrane
permeability, solubility, hemolysis, and non-fouling characteristics on various
disease-relevant targets. In total, our results demonstrate that MCTS-guided
discrete diffusion is a powerful and modular approach for multi-objective
sequence design in discrete state spaces.