Drug Discovery
Peptide and Macrocycle Design
Generate novel peptide sequences and macrocyclic structures with target binding affinity, proteolytic stability, and cell permeability for challenging therapeutic targets.
The Challenge
Why Peptide and Macrocycle Design needs a new approach to generation
Peptide therapeutics occupy a critical middle ground between small molecules and biologics — large enough to engage protein-protein interaction surfaces that small molecules cannot disrupt, yet potentially small enough for intracellular delivery. The design space is vast: sequence, cyclization chemistry, stapling strategies, and non-natural amino acid incorporation all interact to determine binding, stability, and permeability. Current approaches rely on phage display and rational design, which sample the space stochastically or incrementally.
Phage display generates binders but produces linear peptides with poor proteolytic stability and no cell permeability. Rational macrocyclization and stapling improve drug-like properties but are applied post-hoc to existing sequences, creating a sequential optimization bottleneck where improving permeability often compromises binding. Computational peptide design tools score proposed modifications but do not generate novel sequences from therapeutic specifications.
The MatterSpace Approach
How MatterSpace generates for peptide and macrocycle design
MatterSpace Pharma generates peptide and macrocycle candidates through constraint-based sequence construction — specify the target interface, binding affinity requirements, proteolytic stability minimums, and cell permeability constraints, and Pharma constructs sequences with cyclization and modification strategies satisfying all requirements simultaneously.
The Peptide domain pack encodes peptide-protein interaction physics, macrocyclization chemistry, membrane permeability models, and proteolytic stability prediction. Users define therapeutic targets and Pharma generates peptide candidates with predicted binding modes, stability half-lives, and permeability classifications.
Constraint-Based Generation
Specify what the output must satisfy. MatterSpace constructs candidates that meet all constraints simultaneously.
Valid by Construction
Every output satisfies physical laws, stability criteria, and domain constraints — no post-hoc filtering needed.
MatterSpace Pharma
Powered by a domain-specific generation engine with physics-aware priors and adaptive dynamics control.
Generation Output
What MatterSpace generates
- Novel peptide sequences with predicted binding affinities
- Macrocyclic structures with cyclization chemistry specifications
- Stapled peptide candidates with helicity and stability predictions
- Cell-permeable designs with predicted permeability coefficients
Key Differentiators
Why MatterSpace is different
MatterSpace Pharma generates cell-permeable macrocycles by construction, co-optimizing binding affinity, proteolytic stability, and membrane permeability within a single generation pass. The system accesses "undruggable" protein-protein interaction targets by generating peptide architectures that bridge the gap between small molecule reach and biologic specificity.
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Start generating with MatterSpace
Whether you are exploring peptide and macrocycle design for the first time or scaling an existing research programme, MatterSpace generates novel candidates that satisfy your constraints by construction.
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