mlsanityData to 7 Oct 2026

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning

arXiv 2305.08057 ↗First posted 14 May 2023q-bio.BM cs.LG physics.chem-phcited by 5

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Abstract (arXiv)

Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction and the generation of small-molecule conformational ensembles, yet similar progress has not been made for macrocyclic peptides due to their unique properties. Here, we introduce CREMP, a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations. Additionally, we include 3,258 macrocycles with reported passive permeability data to couple conformational ensembles to experiment. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 56 references that are in the map. First posted on arXiv 14 May 2023.

Builds on

15 of 56 references.

References · 15

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3 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 2.

5 papers.

  1. 2024JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensemblescited by 4
  2. 2023Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusioncited by 3
  3. 2025HyperDiffusionFields (HyDiF): Diffusion-Guided Hypernetworks for Learning Implicit Molecular Neural Fieldscited by 0
  4. 2026MMAI Gym for Science: Training Liquid Foundation Models for Drug Discoverycited by 0
  5. 2026EnsembleEGNN: Set-Based Graph Learning for Thermodynamic Ensembles of Cyclic Peptidescited by 0