Next-Generation
Peptide Macrocycles

Up to 80% of drug targets arebeyond the reach of small molecules and biologics. Peptide macrocycles are just the right size to drug them.

Small Molecules

< 800 Da

Oral Delivery

Cell Permeability

High Selectivity

Low Toxicity

PPI Binding

Low Immunogenicity

Peptide Macrocycles

800 -1800 Da
(6-15 AA)

Oral Delivery

Cell Permeability

High Selectivity

Low Toxicity

PPI Binding

Low Immunogenicity

Biologics

> 5000 Da

Oral Delivery

Cell Permeability

High Selectivity

Low Toxicity

PPI Binding

Low Immunogenicity

Menten AI unlocks the potential of peptide therapeutics to tackle diseases beyond traditional therapeutics.

The MAUD 1.0 Platform

Generative AI for De Novo Design of Cyclic Peptides

MAUD 1.0 (Multi-parametetric AI for Unbiased Design) combines generative AI, physics-based modeling, and quantum simulations to design cyclic peptides from scratch.

Unlike traditional machine learning approaches, MAUD 1.0 leverages physics-informed reinforcement learning, eliminating the need for large training datasets while enabling efficient molecular design with atomic precision.

Target Assesment

Molecular Engine

Hit/Lead Optimization

Drug-Like Cyclic Peptides

Target Assessment

Finding the right binding site is key to designing effective peptide therapeutics. Using only a target protein structure as input, MAUD 1.0 analyzes dynamics and structural features to pinpoint optimal sites—including previously unrecognized surfaces—expanding the range of druggable targets.

Molecular Engine

MAUD 1.0’s physics-based generative AI uses the target protein model as input to perform iterative, multi-parameter design cycles that optimize multiple drug-like properties simultaneously, enabling the discovery of potent, cell-permeable, and orally bioavailable therapeutics for challenging targets.

Dive Deeper

Hit/Lead Optimization

Finally, the top hits are taken as inputs for fine-tuning with our optimization engine. MAUD 1.0 focuses on beneficial amino acid substitutions—natural and unnatural—to improve single or multiple properties simultaneously, from potency and stability to permeability. This in silico approach accelerates multi-parameter optimization without requiring experimental structural data, delivering peptide candidates with enhanced drug-like properties faster.

Drug-Like Cyclic Peptides

MAUD 1.0 has demonstrated design of peptide macrocycles with drug-like properties including:

nM Affinity (<100nM)

Oral Bioavailability (>18% F)

Cell Permeability (Log Pe > -6)

High Selectivity

Favourable DMPK

Platform Applications

Transforming today's innovation into tomorrow's life-changing medicines.

Whether it’s designing cell-permeable macrocycles for today's undruggable targets, oral peptides as alternatives to antibody therapeutics, or modulating protein-protein interactions (PPIs) — MAUD 1.0 enables the discovery of peptides that go beyond small molecules and biologics.

Ready to unlock the potential of next-generation peptides?

Let’s talk.

Oral Peptides

Design cyclic peptide inhibitors against extracellular or intracellular targets with oral bioavailability as a parameter.

Cell-Permeable Peptides

Apply Menten AI’s proprietary platform to design cyclic peptides against intracellular targets with a key focus on PPI disruption.

Molecular Glues & Bi-specifics

Functionalize and conjugate peptides to generate multi-specific peptide binders with diverse applications.

Computational Optimization

Perform in silico structure-based optimization with multi-parameter enrichment of drug properties.

Scientific Materials

Take a look at the most recent publications by our team members

2025

Heuristic energy-based cyclic peptide design

PLoS Computational Biology, 30 Apr 2025, 21(4):e1012290

Zhu Q, Mulligan VK, Shasha D

CyclicCAE: A Conformational Autoencoder for Efficient Heterochiral Macrocyclic Backbone Sampling

bioRxiv, 2025, pp. 2025.02. 21.639569

Powers AC, P Renfrew PD, Hosseinzadeh P, Mulligan VK

2024

Ultra-confined controllable cyclic peptides as supramolecular biomaterials

Nano Today, 2024, pp. 102247

Chorsi M, Linthicum W, Pozhidaeva A, Mundrane C, Mulligan VK, Chen Y, Tavousi P, Gorbatyuk V, Vinogradova O, Hoch JC, Huey BD, Nguyen TD, Soh HT, Kazerounian K, Ilies H

High-resolution epitope mapping of commercial antibodies to ANCA antigens by yeast surface display

Journal of Immunological Methods, 2024, pp. 113654

Poulton JS, Lamba S, Free M, Xi G, McInnis E, Williams G, Kudlacek ST, Thieker D, Kuhlman B, Falk R

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

PLOS Computational Biology, 2024, pp. e1011939

Ertelt M, Mulligan VK, Maguire JB, Lyskov S, Moretti R, Schiffner T, Meiler J, Schoeder CT

Heteropolymer modelling and design incorporating quantum chemistry with RosettaQM

APS March Meeting Abstracts, 2024, pp. W55. 008

Mulligan VK, Turzo B, P, Renfrew D, Jurich C, Brown B

Next-Generation Peptide Macrocycles

Small Molecules

Peptide Macrocycles

Biologics

Menten AI unlocks the potential of peptide therapeutics to tackle diseases beyond traditional therapeutics.

The MAUD 1.0 Platform

Generative AI for De Novo Design of Cyclic Peptides

Target Assessment

Molecular Engine

MAUD 1.0 - Molecular Engine

Physics-based generative AI de novo design engine - click on each section to learn more

Hit/Lead Optimization

Drug-Like Cyclic Peptides

MAUD 1.0 has demonstrated design of peptide macrocycles with drug-like properties including:

Platform Applications

Transforming today's innovation into tomorrow's life-changing medicines.

Oral Peptides

Cell-Permeable Peptides

Molecular Glues & Bi-specifics

Computational Optimization

Scientific Materials

2025

Heuristic energy-based cyclic peptide design

CyclicCAE: A Conformational Autoencoder for Efficient Heterochiral Macrocyclic Backbone Sampling

2024

Ultra-confined controllable cyclic peptides as supramolecular biomaterials

High-resolution epitope mapping of commercial antibodies to ANCA antigens by yeast surface display

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Heteropolymer modelling and design incorporating quantum chemistry with RosettaQM

2023

Macrocyclic polypeptides

De novo designed mixed chirality peptide macrocycles with internal symmetry

Macromolecular modelling algorithms harnessing quantum computation

Population dynamics of immunological synapse formation induced by bispecific T cell engagers predict clinical pharmacodynamics and treatment resistance

2022

Macrocyclic polypeptides

"How to Design Peptides", Chemokine-Glycosaminoglycan Interactions: Methods and Protocols

Multibody molecular docking on a quantum annealer

Multibody molecular docking on a quantum annealer

"Computational methods for peptide macrocycle drug design", Peptide therapeutics: fundamentals of design, development, and delivery

Stabilizing proteins, simplified: A Rosetta‐based webtool for predicting favorable mutations

Accurate de novo design of membrane-traversing macrocycles

Accurate de novo design of membrane-traversing macrocycles

Rational design of rigidly-folded peptide macrocycle therapeutics using classical and quantum computers

Correction to “the rosetta all-atom energy function for macromolecular modeling and design”

Ancestral Sequence Reconstruction and Alternate Amino Acid States Guide Protein Library Design for Directed Evolution

Designing new peptide and protein therapeutics using adiabatic quantum annealers

Peptide design with quantum approximate optimization algorithm

"Computational Design of Peptide-Based Binders to Therapeutic Targets", Approaching the Next Inflection in Peptide Therapeutics: Attaining Cell Permeability and Oral Bioavailability

2021

Structure-Guided Stabilized DENV2 Envelope Subunit Vaccine Elicits DENV2 Neutralizing Antibody In Vivo

Designed, highly expressing, thermostable dengue virus 2 envelope protein dimers elicit quaternary epitope antibodies

XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers

Current directions in combining simulation-based macromolecular modeling approaches with deep learning

Perturbing the energy landscape for improved packing during computational protein design

Augmenting QAOA Ansatz with Multiparameter Problem-Independent Layer.

Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1

2020

Computational design of mixed chirality peptide macrocycles with internal symmetry

Computational Stabilization of T Cell Receptors Allows Pairing with Antibodies to Form Bispecifics

The Emerging Role of Computational Design in Peptide Macrocycle Drug Discovery

The Emerging Role of Computational Design in Peptide Macrocycle Drug Discovery

Computer-Based Design and Stabilization of the Dengue Virus Envelope Protein for Use as a Subunit Vaccine

Mathematical Models of Protease-Based Enzymatic Biosensors

2019

Designing Peptides on a Quantum Computer

De Novo Design of Bioactive Protein Switches

2018

Programmable Design of Orthogonal Protein Heterodimers

Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design

Accurate Computational Design of Multipass Transmembrane Proteins

2017

Comprehensive Computational Design of Ordered Peptide Macrocycles

2016

Virus-Enabled Biosensor for Human Serum Albumin

Accurate De Novo Design of Hyperstable Constrained Peptides

2015

Shear-Stress-Mediated Refolding of Proteins from Aggregates and Inclusion Bodies

Next-Generation
Peptide Macrocycles

Physics-based generative AI de novo design engine
- click on each section to learn more