Our group is speeding up an integrated data-driven and intelligence-guided research paradigm for the next generation of chiral biomaterials. We aim to establish a closed-loop framework that bridges computational prediction, molecular design, experimental synthesis, and performance validation, with a targeted focus on programming chirality-mediated material-biological interactions.

Vision: The AI-Guided Chiral Biomaterials Discovery Cycle

We are shifting from conventional trial-and-error approaches to an AI-predictive paradigm for chiral biomaterial innovation. Our objective is to deploy artificial intelligence to decode the complex correlations between chiral molecular architecture (especially C₂-symmetric motifs), self-assembly behavior, and emergent biological functions—most critically, the stereospecific recognition and interaction with biological targets such as protein active sites.

Key Research Thrusts

• AI/ML-Powered Molecular Design and Functional Prediction

A: Target-First Design: We develop AI models that begin with the 3D chiral features of target protein active sites. Using deep learning (e.g., convolutional neural networks) and transfer learning, we generate and optimize C2-symmetric chiral molecular building blocks with tailored spatial compatibility for specific biological targets (e.g., enzymes, immune receptors).

B: Quantitative Structure-Property Relationship (QSPR) Modeling: We leverage advanced machine learning algorithms (e.g., graph neural networks, transformer models) to build high-fidelity predictive models. These models correlate molecular descriptors (e.g., solvent parameters, topological indices, electronic structure features) with key material properties, such as binding affinity, catalytic activity, or immune activation efficiency.

C: Assembly Pathway Prediction: We utilize molecular dynamics (MD) and coarse-grained simulations, guided by AI with molecular dynamics and Monte Carlo simulations, to predict and understand the chirality transfer and hierarchical assembly mechanisms from designed molecules to functional supramaterials (e.g., nanofibers, hydrogels).

• High-Throughput Experimentation (HTE) for Validation and Data Generation

A: Closed-Loop Learning: We establish a high-throughput experimental platform for rapid synthesis, assembly, and characterization of AI-designed chiral molecules. This generates rich, standardized datasets that are fed back to continuously refine and validate our computational models.

B: Mechanistic Probing: HTE allows us to systematically investigate the non-covalent interactions and thermodynamic parameters (e.g., binding constants, enthalpy/entropy changes) governing the chiral material-target interface, moving beyond correlation to causation.

• Deciphering the Chiral Biointerface Mechanism

A central focus is understanding the molecular mechanism of spatial adaptation between chiral materials and protein active sites. We combine:

• Advanced Simulations: All-atom MD and free energy calculations to visualize binding dynamics and chiral recognition.
• Mechanistic Models: Developing models to explain how chiral materials modulate biological outcomes (e.g., inducing pyroptosis via mitochondrial targeting, evading immune clearance via protein corona inhibition) based on their stereospecific fit.
• "Lab-in-the-Loop" AI: Integrating experimental readouts directly into adaptive AI training cycles.

Methodological Integration: A Synergistic Workflow

• Target Analysis & Feature Learning: Extract spatial and chemical features from target protein structures.
• AI-Driven Design: Use neural networks to propose optimal chiral molecular motifs.
• Pathway Optimization & Simulation: AI suggests synthetic and assembly pathways; simulations preview outcomes.
• High-Throughput Synthesis & Testing: Automated platforms rapidly create and characterize materials.
• Performance Data & Mechanism Feedback: Biological assay results and mechanistic insights feed back to Step 1, closing the loop.

Expected Outcomes and Impact

This intelligent design framework will enable us to:

Rationally design chiral materials with unprecedented precision for applications in catalysis, biosensing, drug delivery, and immunotherapy. Fundamentally understand the "chiral code" that governs material-biology communication. Dramatically accelerate the discovery timeline for advanced chiral biomaterials, translating fundamental chiral science into transformative technologies.

In essence, we are building a future-oriented research engine where artificial intelligence, advanced simulation, and automated experimentation converge to master the design of chiral matter, unlocking its full potential for science and medicine.