Data Collection and Curation
Large datasets of known cyclic peptides and their biological activities are compiled from public databases and scientific literature. These datasets form the foundation for model training.

Molecular Representation
Peptides are encoded using sequence-based formats (e.g., SMILES, one-letter amino acid codes) or structure-based descriptors (e.g., 3D conformations, graph representations). Advanced models may incorporate both sequence and structural information.

Generative Modeling
Deep generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), or transformer-based language models, are trained to learn the underlying patterns of bioactive cyclic peptides. These models can generate novel peptide sequences predicted to have desirable properties such as stability, target affinity, or membrane permeability.

Property Prediction and Optimization
Predictive ML models are used to evaluate generated peptides for specific properties (e.g., binding affinity, toxicity, solubility). Reinforcement learning or multi-objective optimization algorithms can then guide the generation toward peptides with optimal profiles.

Structure Modeling and Validation
For promising candidates, molecular dynamics simulations and AI-powered structure prediction tools (such as AlphaFold or Rosetta) are employed to confirm stable cyclic conformations and assess target interactions.

Targeting and Selectivity
By attaching ligands or antibodies via linkers, cyclic peptides can be directed to specific tissues, cell types, or receptors. This targeted delivery reduces off-target effects and enhances therapeutic efficacy, particularly in oncology and neurological applications.

Multivalency and Synergistic Function
Linkers can connect multiple cyclic peptide units or couple them with other bioactive molecules, enabling multivalent interactions with a single target or multiple targets simultaneously. This can enhance binding affinity and create synergistic therapeutic effects.

Payload Conjugation
In drug conjugates (e.g., peptide–drug conjugates, PDCs), linkers allow the cyclic peptide to carry cytotoxic agents, small-molecule drugs, or siRNAs to specific intracellular environments. The linker can be cleavable or stable, depending on the desired release profile.

Controlled Pharmacokinetics and Release
Linkers can be designed to respond to specific stimuli (pH, enzymes, redox environment), allowing controlled or site-specific drug release. This enhances the safety and precision of cyclic peptide-based therapeutics.

Structural Flexibility and Spatial Orientation
Proper linker design ensures that the appended moieties do not interfere with the cyclic peptide’s conformation or target-binding interface. Spacer length, rigidity, and hydrophilicity can all be tuned to preserve or enhance biological function.