Expanding the Limits of in silico Macrocycle Design

Due to their great potential in many drug discovery projects, significant efforts have been made to explore both natural and synthetic drug-like molecules that contain macrocycles.  In the past, macrocycle design has proved to be challenging both synthetically and computationally.  Computationally, chemical software is not typically designed for large flexible rings, but Schrödinger software provides several macrocycle design solutions including the ability to perform sampling using Prime, predict permeability, and dock macrocycles using Glide. It also enables users to estimate ring strain as well as compare relative affinities between compounds using FEP+ making macrocycle design more accessible than ever before.



Sampling with Prime:

Using technology adapted from hierarchical protein loop sampling, Prime macrocyle sampling for small macrocycles is:

  • Accurate – Median backbone RMSD of 0.40 Å, with few outliers over 1.5 Å
  • Diverse – Output conformations are unique
  • Fast – Median calculation times are 5-10 minutes
  • Scalable – Providing accurate and diverse results for cyclic peptides and macrocyclic natural products

Passive Membrane Permeability:

Permeability of macrocyclic and non-macrocyclic molecules can be predicted by approximating the transfer free energy of molecules between water and a membrane using macrocycle sampling and fast implicit solvent models.

Docking with Glide:

Integrating macrocycle ring sampling into Glide docking creates a protocol that is:

  • Accurate – 65% of cases within 2.0 Å RMSD for self-docking of macrocycles
  • Fast – About an hour, on average, per compound including ring sampling and docking

Ring Stability Prediction:

Our predictions of the macrocycle ring strain required to adopt conformations similar to that of a known active ligand can be used to screen linkers to find those that will result in high-affinity macrocyclic versions of linear ligands.

Free Energy Perturbation with FEP+:

FEP+ can compare the relative affinity of congeneric macrocyclic and non-macrocyclic compounds, even when the difference is in the ring or the creation of a ring.


  1. “Automated Design of Macrocycles for Therapeutic Applications: From Small Molecules to Peptides and Proteins”

    Sindhikara, S.; Wagner, M.; Gkeka, P.; Güssregen, S.; Tiwari, G.; Hessler, G.; Yapici, E.; Li, Z.; Evers, E.;, J. Med. Chem., 2020, X, x-x.

  2. “Evaluation of Free Energy Calculations for the Prioritization of Macrocycle Synthesis”

    Paulsen, J.L.; Yu, H.S.; Sindhikara, D.; Wang, L.; Appleby, T.C.; Villaseñor, A.G.; Schmitz, U.; Shivakumar, D., J. Chem. Inf. Model., 2020, 60 (7), 3489–3498.

  3. “High throughput evaluation of macrocyclization strategies for conformer stabilization”

    Sindhikara, D. and Borrelli, K., Nature, Scientific Reports , 2018, 8 (6585), doi:10.1038/s41598-018-24766-5.


  1. Sindhikara, D.; Spronk, S.A.; Day, T.; Borrelli, K.; Cheney, D.L.; Posy, S.L., “Improving Accuracy, Diversity, and Speed with Prime Macrocycle Conformational Sampling,”

    J. Chem. Inf. Model., 2017, 57(8), pp 1881-1894.

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