title: PepTune: De Novo Generation of Therapeutic Peptides with
    Multi-Objective-Guided Discrete Diffusion
summary: 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.
paper_authors._id: 676d74cd96a84bb36f9d04e1
paper_authors.name: Sophia Tang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-13T10:27:33.961Z
paper_authors._id: 66d89392c81167fc5e1bdada
paper_authors.avatarUrl: /avatars/4d06f2abec11e471a918353d687f77fe.svg
paper_authors.fullname: Sophia Tang
paper_authors.type: user
paper_authors.user: sophtang
paper_authors._id: 676d74cd96a84bb36f9d04e2
paper_authors.name: Yinuo Zhang
paper_authors._id: 676d74cd96a84bb36f9d04e3
paper_authors.name: Pranam Chatterjee
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-12-26T16:27:18.389Z
paper_authors._id: 64cd5b3f0494187a9e8b7c69
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eo9HiGbvCxHQZ-QJB1Y_o.jpeg
paper_authors.fullname: Pranam Chatterjee
paper_authors.type: user
paper_authors.user: pranamanam
paper_title: PepTune: De Novo Generation of Therapeutic Peptides with
    Multi-Objective-Guided Discrete Diffusion
paper_summary: 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.
paper_ai_summary: PepTune, a multi-objective discrete diffusion model based on MDLM, uses Monte Carlo Tree Search to generate diverse, optimized therapeutic peptides with multiple desirable properties.
paper_ai_keywords: discrete diffusion model Masked Discrete Language Model (MDLM) Monte Carlo Tree Search (MCTS) PepTune target binding affinity membrane permeability solubility hemolysis non-fouling characteristics Pareto-optimal sequences discrete sequence optimization