Peptide Engineering for Therapeutic Discovery
AI-assisted peptide design, optimization, and developability assessment to reduce experimental cycles and downstream attrition in peptide-based therapeutics.
AI-Powered Peptide Engineering Capabilities
Learn moreDe Novo & Guided Peptide Design
Transformer- and diffusion-based sequence generation enables de novo and guided peptide design under explicit constraints such as length, charge, sequence motifs, and target interaction requirements.
Peptide-Protein Interaction Modeling
Interface scoring and hotspot identification are used to prioritize biologically plausible binding modes for peptide modulators or inhibitors.
In Silico Optimization & Mutagenesis
Multi-objective optimization simultaneously balances affinity, stability, and solubility, with rational mutation proposals generated in a fully traceable manner.
Structure & Conformation Prediction
Sequence-to-structure modeling captures peptide conformational ensembles rather than relying on single static structures, improving relevance for flexible and disordered peptides.
Developability & Liability Prediction
Early prediction of aggregation, degradation, and immunogenicity signals highlights manufacturability and formulation risks before experimental validation.
Integration with Experimental Pipelines
Optimized peptide variants are delivered as synthesis-ready lists designed to integrate seamlessly with CRO workflows or internal wet-lab pipelines.
10×
larger virtual screening spaceCompared to manual or heuristic peptide design
50–70%
fewer wet-lab iterationsThrough early in silico triage
2-3×
improvement in hit qualityWhen ranking by affinity and developability
2×
faster progression to lead candidatesWeeks instead of quarters in early optimization
AI-Driven Drug Discovery for Enamine’s 36B Molecule Library
The system combined machine learning–based affinity prediction, large-scale virtual screening, and docking simulations (DiffDock) with active learning loop
Check TestimonialsBiotech
Industry
USA
Location
Drug Discovery, AI compound screening, Chemical space optimization
Services
$200,000 to $999,999
Budget
The team showed initiative and proactiveness in developing alternative solutions to reach our goals. As a result, we obtained very complex and high-quality support.
Under NDA
CEO, Chemistry Solutions Company
Our Peptide Engineering
Workflow
Problem Definition & Constraints
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Target biology, mechanism of action, and intended modulation strategy
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Peptide class constraints (length range, linear vs. cyclic, chemical modifications)
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Practical synthesis and assay constraints defined by internal teams or CRO partners
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Developability criteria, including solubility, stability, aggregation risk, and formulation considerations
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Structural or interaction context (known binding sites, interfaces, or templates)
Design & Variant Generation
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De novo generation for unexplored sequence space or novel mechanisms
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Guided design informed by known motifs, structural templates, or reference peptides
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Library generation at scales typically ranging from 10⁴ to 10⁶ variants
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Explicit control over sequence diversity, charge distribution, and physicochemical properties
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Metadata completeness, validate raw omics inputs, detect batch effects, harmonize files across heterogeneous sources, and consistently enforce internal SOP rules.
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Full traceability between generated variants and the constraints that produced them
In Silico Screening & Ranking
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Peptide–target interaction scoring where structural context is available
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Stability and conformational behavior assessment for flexible or disordered peptides
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Solubility, aggregation, and degradation risk estimation
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Early liability and developability signal detection
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Composite ranking that balances biological activity with downstream feasibility
Shortlist for Synthesis & Iterative Refinement
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Selection of a small, explainable subset of top-ranked peptides
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Clear rationale for inclusion of each candidate (binding, stability, or risk profile)
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Optional diversification to hedge against model uncertainty
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Export-ready variant lists compatible with internal synthesis pipelines or CRO workflows
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Incorporation of wet-lab results into model updates when available
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Re-weighting of objectives based on observed assay outcomes
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Progressive narrowing of design space across iterations
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Key R&D Challenges
We Address
Higher Functional Hit Probability per Experimental Round
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Prioritization of high-confidence peptide variants before synthesis
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Reduction of low-information experimental cycles driven by random or heuristic design
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Focus on candidates with balanced activity and physicochemical profiles
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More efficient allocation of wet-lab resources per discovery sprint
Avoiding Unstable or Non-Viable Peptide Sequences
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Early identification of aggregation-prone and degradation-prone sequences
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Assessment of conformational stability for flexible or disordered peptides
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Filtering of sequences with unfavorable solubility or formulation characteristics
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Reduced attrition caused by synthesis or purification failures
Rational Design of Peptide Modulators and Inhibitors
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Mapping of peptide-protein interaction interfaces where structural context is available
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Identification of binding hotspots relevant to modulation or inhibition
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Optimization of sequence variants around biologically meaningful interactions
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Improved interpretability of why specific candidates are selected
Lowering Manufacturing and Formulation Risk Early
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Early detection of manufacturability and scale-up risks
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Identification of liabilities related to aggregation, stability, or chemical modifications
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Alignment of design decisions with downstream formulation constraints
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Fewer late-stage redesigns driven by production limitations
Evaluate peptide feasibility and risk before experiments.
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