PepTune: De Novo Generation of Therapeutic Peptides with
  Multi-Objective-Guided Discrete Diffusion
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.
676d74cd96a84bb36f9d04e1
Sophia Tang
claimed_verified
2025-05-13T10:27:33.961Z
66d89392c81167fc5e1bdada
/avatars/4d06f2abec11e471a918353d687f77fe.svg
Sophia Tang
user
sophtang
676d74cd96a84bb36f9d04e2
Yinuo Zhang
676d74cd96a84bb36f9d04e3
Pranam Chatterjee
extracted_confirmed
2024-12-26T16:27:18.389Z
64cd5b3f0494187a9e8b7c69
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eo9HiGbvCxHQZ-QJB1Y_o.jpeg
Pranam Chatterjee
user
pranamanam
PepTune: De Novo Generation of Therapeutic Peptides with
  Multi-Objective-Guided Discrete Diffusion
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.
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.
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