Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
Authors: Dapeng Jiang, Xiangzhe Kong, Jiaqi Han, Mingyu Li, Rui Jiao, Wenbing Huang, Stefano Ermon, Jianzhu Ma, Yang Liu
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our model, despite trained with linear peptides, is capable of generating diverse target-binding cyclic peptides, reaching success rates from 38% to 84% on different cyclization strategies. 4. Experiments |
| Researcher Affiliation | Academia | 1 Institute for AI Industry Research (AIR), Tsinghua 2 Xingjian College, Tsinghua University 3 Department of Computer Science and Technology, Tsinghua University 4 Stanford University 5 Gaoling School of Artificial Intelligence, Renmin University of China 6 Beijing Key Laboratory of Research on Large Models and Intelligent Governance 7 Department of Electronic Engineering, Tsinghua University. |
| Pseudocode | Yes | Algorithm 1 Training Procedure of CP-Composer Algorithm 2 Inference Procedure of CP-Composer |
| Open Source Code | Yes | F. Code Availability The codes for our CP-Composer is provided at the link https://github.com/jdp22/CP-Composer_final. |
| Open Datasets | Yes | Dataset. We utilize Pep Bench and Prot Frag datasets (Kong et al., 2024) for training and validation, with the LNR dataset (Kong et al., 2024; Tsaban et al., 2022) for testing. |
| Dataset Splits | Yes | We utilize Pep Bench and Prot Frag datasets (Kong et al., 2024) for training and validation, with the LNR dataset (Kong et al., 2024; Tsaban et al., 2022) for testing. Pep Bench contains 4,157 protein-peptide complexes for training and 114 complexes for validation, with a target protein longer than 30 residues and a peptide binder between 4 to 25 residues. |
| Hardware Specification | Yes | We train CP-Composer on a 24G memory RTX 3090 GPU with Adam W optimizer. ...Simulations are conducted using the Amber22 package with the CUDA implementation of particle-mesh Ewald (PME) MD and executed on Ge Force RTX 4090 GPUs (Salomon-Ferrer et al., 2013). |
| Software Dependencies | Yes | Simulations are conducted using the Amber22 package with the CUDA implementation of particle-mesh Ewald (PME) MD and executed on Ge Force RTX 4090 GPUs (Salomon-Ferrer et al., 2013). ...For system preparation, the ff14SB force field is applied to proteins and peptides (Maier et al., 2015). |
| Experiment Setup | Yes | The initial learning rate is set to 10 4 and is reduced by a factor of 0.8 if the validation loss does not improve for 5 consecutive epochs. Regarding the diffusion model, we train for no more than 1000 epochs. The learning rate is 10 4 and decay by 0.6 and early stop the training process if the validation loss does not decrease for 10 epochs. During the training process, we set the guidance strength as 1 for sampling at the validation stage. ...For the RBF kernel, we use 32 feature channels. |