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Figure 1. 

AI powered stages in polysaccharide drug development. The diagram summarizes the integration of AI methodologies across the pipeline of polysaccharide-based drug discovery and development, highlighting how data-driven approaches accelerate each stage from raw material processing to bioactivity prediction.

Figure 2. 

Schematic illustration of glycan representation learning. (a) SweetTalk: a recurrent neural network (RNN)-based language model. GW: glycoword (three monosaccharides and two bonds). Glycans are featurized by extracting GWs, whose embeddings are obtained by averaging their constituent glycoletter embeddings. The corresponding embeddings for whole glycans are then constructed by averaging GW embeddings for downstream tasks. (b) SweetNet, GlycanAA, and GlycanGT: three graph-based glycan encoders with distinct node/edge definitions and information propagation strategies. SweetNet uses monosaccharides and linkages as nodes with connections between them as edges and performs local message passing via graph convolutional layers. GlycanAA constructs a heterogeneous graph with atom and monosaccharide nodes, connected by atom-atom, atom-monosaccharide, and monosaccharide-monosaccharide edges, enabling hierarchical message passing from the atomic to the monosaccharide level. GlycanGT treats monosaccharides and glycosidic bonds as tokens and captures global context through full self-attention.

Figure 3. 

Application of AI in the analysis of polysaccharide functions. (a) AI in polysaccharide structure–activity relationship research: structural features (composition, branching, molecular weight) are processed by machine learning models (graph neural networks, random forests) to predict biological activities (anti-inflammatory, anti-tumor, hypoglycemic). (b) AI in polysaccharide target prediction research: integration of chemical structures with molecular docking to identify protein targets (e.g., CTSG, LTF, MPO, PRTN3) linked to specific therapeutic results like antipyresis. (c) AI in polysaccharide mechanism prediction research: data from literature and databases (GlyTouCan, UniCarb-DB) are utilized via deep learning to predict functional mechanisms, identifying glycan motifs associated with immunogenicity, pathogenicity, and immune evasion. (d) AI in polysaccharide delivery carriers and pharmaceutical dressings: evaluation of polysaccharide-based biomaterials with key properties (biocompatibility, antibacterial, pro-healing) across consecutive stages of material application, from implantation to tissue regeneration.

Figure 4. 

Challenges and future directions of AI in polysaccharide research. (a) De novo polysaccharide design: Illustration of using AI to generate completely novel polysaccharide molecular structures, highlighting the challenge and goal of achieving an 'Optimized structure'. (b) Phenotypic function prediction: schematic of the workflow mapping Cell phenotype input' through an AI model to hit a specific 'Target', resulting in a 'Predicted function' for biological activity. (c) Polysaccharide metabolism prediction: representation of human metabolic tracking, highlighting current bottlenecks such as 'Insufficient data' and a 'Model inexplicable', (lack of interpretability) within the predictive pipeline. (d) Clinical evaluation of polysaccharide drugs: demonstration of AI acting as an 'Auxiliary clinical trial' tool to simulate patient group cohorts, aimed at ensuring 'Effective' and 'Safe' outcomes for polysaccharide-based therapeutics.