Jul 2026· Journal of Chemical Information and Modeling· Vol 66, pp. 8545-8560· 0 citations· 55 references
Medicine
TL;DR
A retrospective validation of the FEP+ framework across five diverse macrocyclic and cyclic peptide inhibitor series is presented, establishing FEP+ as an effective computational method for derisking and accelerating the discovery of clinically viable bRo5 therapeutics.
Abstract
Macrocycles and cyclic peptides represent a compelling therapeutic modality for engaging historically challenging biological targets, yet their inherent structural complexity and laborious synthetic demands pose significant hurdles in drug discovery. Given the high resource investment required for macrocyclic synthesis, the integration of rigorous, physics-based computational methods provides a critical advantage for accurately prioritizing design candidates. While Free Energy Perturbation (FEP) has emerged as a transformative method for affinity prediction, its application to complex, beyond-rule-of-five (bRo5) macrocycles demands specialized enhanced sampling protocols and careful consideration of receptor conformational states to effectively navigate their multidimensional conformational landscapes. Here, we present a retrospective validation of the FEP+ framework across five diverse macrocyclic and cyclic peptide inhibitor series: KRAS, PCSK9, MCL-1, JAK2, and Cyclin A/B. Encompassing over 230 unique peptidic and nonpeptidic analogues, our analysis of this consolidated data set demonstrates robust predictive accuracy (global pairwise RMSEΔΔG = 1.06 kcal/mol) and reveals critical insights into the complex interplay between ligand preorganization, hydration dynamics, and binding energetics. The results demonstrate reliable intraseries rank-ordering and robust absolute accuracy across an experimental dynamic range exceeding 10 kcal/mol (>7 orders of magnitude in binding affinity), establishing FEP+ as an effective computational method for derisking and accelerating the discovery of clinically viable bRo5 therapeutics.
Natural cyclic peptides have long served as a rich reservoir of bioactivity, occupying a unique region of the drug space that bridges the gap between small molecules and large biologics. Evolution has perfected the macrocyclic architecture to achieve exceptional target specificity and metabolic stability, providing a structural blueprint that allows these molecules to engage extended protein surfaces often inaccessible to conventional drugs. While early landmarks like cyclosporin A demonstrated the power of chameleonicity, the ability to adapt conformations to different environments - the field is currently undergoing a paradigm shift. Nature is no longer viewed merely as a source of lead compounds to be mined, but as a conceptual framework for de novo design. By integrating natural principles such as conformational constraint and amide-masking with cutting-edge technologies like mRNA display (e.g. RaPID) and artificial intelligence, researchers are now rationally engineering next-generation macrocycles, positioning them at the frontier of modern drug development.
Greta Bergamaschi, Giulia Lodigiani, Stefano Gandolfi et al.· Current Opinion in Chemical...· 0 citations
Peptidomimetics have matured from motif‑based inhibitors into a structural engineering discipline that systematically translates peptide recognition surfaces into drug‑like scaffolds. Driven by the urgent clinical demand to overcome the inherent pharmacological liabilities of biomolecules, the field is undergoing a decisive Peptide-to-Small Molecule paradigm shift-functionally converting peptide-derived recognition motifs into orally bioavailable synthetic therapeutics. This Perspective highlights how foundational geometric design principles-linear repetition, convergent fusion, and cyclization-define next‑generation architectures capable of targeting complex protein-protein interactions (PPIs). Repeating‑unit oligomers exemplify linear projection strategies, heterocycle‑centered scaffolds embody the convergent fusion of recognition motifs, and macrocyclic frameworks pre-organize bioactive conformations while enabling access to non‑canonical topologies. Beyond simple mimicry, these architectures increasingly embrace dynamic responsiveness, aggregation remodeling, and universal multi‑structure platforms. We argue that the convergence of geometric logic with automated synthesis and AI‑driven design will transform peptidomimetics into a primary modality for decoding and therapeutically engaging the human interactome, including historically "undruggable" PPIs.
Macrocycles are highly privileged scaffolds in contemporary drug discovery, yet their inherent structural flexibility often imposes severe entropic penalties upon target binding. We report the rational design, synthesis, and comprehensive biological evaluation of a novel class of macrocyclic ureas that incorporate an unusual sp3-rich constraint. Our scalable synthetic strategy features a highly efficient palladium-catalyzed Buchwald-Hartwig intramolecular macrocyclization to assemble the rigid backbone. Rigorous structural characterization, utilizing NOESY NMR and X-ray crystallography, unambiguously confirmed that this geometric restriction forces the molecule into a tightly puckered, internally shielded, pre-organized conformation. Our in vitro biological screening demonstrated a profound amplification in target potency compared to unconstrained flexible analogues. Systematic structure-activity relationship profiling identified a fluorinated piperidine derivative as the principal lead candidate, delivering a truly outstanding nanomolar affinity with an IC50 of 42 nM. Furthermore, physicochemical profiling revealed that this constrained framework induces favorable chameleonic behavior, perfectly balancing high aqueous solubility with exceptional passive lipid membrane permeability and extended metabolic stability. Ultimately, this multidisciplinary study provides a validated, highly translatable blueprint for successfully leveraging unusual structural constraints to discover potent, orally bioavailable macrocyclic therapeutics.
P. M.R., Palati Divya Jyothi, G. Hemalatha et al.· Genetics and Molecular Resea...· 0 citations
HighPlay2 is presented as a feasible framework for the early-stage design and screening of cyclic peptide candidates containing ncAAs, while further affinity maturation and experimental structural validation remain necessary.
Huitian Lin, Wentong Wang, Ning Zhu et al.· European journal of medicina...· 0 citations
Cyclic peptides are a promising therapeutic modality, offering the potential to target challenging intracellular protein-protein interactions involved in cancer and other diseases. However, their clinical utility is frequently restricted by poor membrane permeability. While deep learning offers new methodologies to predict permeability, current models are limited by a reliance on 2D molecular representations that fail to capture the conformational flexibility inherent to macrocycles. Existing 3D resources also lack physics-based sampling of conformational dynamics across solvent environments that are critical for membrane permeability. To bridge this gap, we present CycPeptMPDB-4D, a comprehensive dataset comprising atomistic molecular dynamics trajectories for 5,160 structurally diverse cyclic peptides, including unnatural, N-methylated, and D-residues in circle and lariat topologies. Each peptide was simulated using the AMBER14SB force field in both explicit water and hexane environments for 50 nanoseconds to generate conformational ensembles in aqueous and membrane-mimicking phases. The trajectories capture the “chameleon-like” property, evidenced by markedly reduced conformational flexibility and polar surface area in the hydrophobic phase. Technical validation demonstrates that the simulated ensembles are in high agreement with experimental NMR data, covering NMR conformers within an RMSD of 1.6 Å. The dataset provides clustered ensembles, representative structures, and specialized descriptors such as desolvation free energy. This resource is designed to facilitate the development of deep learning models that incorporate 3D or 4D (trajectory- or ensemble-based) information to improve the prediction of cyclic peptide membrane permeability.
Wei Liu, Nguyen Hung Pham, Chandra S. Verma et al.· Scientific Data· 0 citations
This manuscript traces my journey through computational medicinal chemistry, showing how mechanistic and structural insights and physicochemical reasoning enable the translation of challenging targets into drugs and general design principles. The central theme, conformational analysis, links on-target potency via pre-organization of the bioactive conformation with physics-based physicochemical property prediction. This strategy can unlock dramatic gains in lipophilic efficiency and pharmacokinetic properties through the judicious addition of single atoms. This principle is extended to proteins, particularly kinases, where discrete conformational states can explain binding modes and kinetics. Binding to inactive conformations is linked to slow-on/ slow-off kinetics and can be engineered through ligand design or protein mutations. The manuscript summarizes principles of oral bioavailability in beyond-Rule-of-5 (bRo5) space, highlighting neutral polarity as a key determinant of permeability and exposure. Marketed oral bRo5 drugs and the lead optimization campaigns of first in class representatives converge on a polarity-lipophilicity sweet spot. Finally, nonclassical zwitterions represent a general design strategy to reconcile low lipophilicity with high permeability, supported by strong agreement between computation and experiment.