Integrating multi-physics simulation with AI performance prediction, the platform optimizes composition and degradation control for materials including PLLA microspheres, ECM scaffolds, and hydrogels, while extending to Collagen 3D helical structure design to enhance yield and prolong in vivo degradation profiles.

Computation-guided experimentation reduces experimental workload by over 50%, shortens Class III medical device registration cycles by more than one year, and has supported multiple products in advancing to clinical translation.

Integrating deep learning with multi-scale biophysical simulations, the platform enables de novo generation of high-activity antibodies directly from antigen epitopes, addressing undruggable targets and navigating patent barriers.

The discovery cycle is reduced from 2–3 years to 6–12 months, with R&D costs lowered by 70%, while maintaining high prediction accuracy even for novel targets with no prior data.

Integrating AI with bioinformatics algorithms, the platform rapidly identifies potential targets amenable to small nucleic acid interference and intelligently designs candidate sequences—including siRNA and ASO—with precise prediction of silencing efficacy and off-target risks, enabling highly specific optimization.

Multi-dimensional delivery strategy assessments are provided for diverse tissues and organs, including liver and CNS, accelerating the clinical translation of candidate molecules.

Integrating AI with molecular dynamics simulations, the platform enables intelligent structural design and functional prediction of enzyme proteins, rapidly optimizing catalytic activity, thermostability, and substrate specificity—broadening the enzymatic properties and application boundaries.

Precise prediction of degradation risks under in vitro conditions guides mutagenesis and process optimization, accelerating the industrial-scale application of enzyme preparations.

Backed by tens of millions of real experimental data points—covering multidimensional parameters including imaging, morphology, flow cytometry, protein expression, and expansion kinetics—and encompassing a full spectrum of cell therapy products such as MSC, TSPC, iPSC, NK, and CAR-T, the platform continuously feeds data back into algorithm refinement.

With deep involvement in multiple industry standard-setting initiatives, the platform has reduced R&D timelines by 40% and doubled success rates, transforming cell therapy development from experience-based trial-and-error to data-driven precision.

Integrating multi-physics simulation with AI performance prediction, the platform optimizes composition and degradation control for materials including PLLA microspheres, ECM scaffolds, and hydrogels, while extending to Collagen 3D helical structure design to enhance yield and prolong in vivo degradation profiles.

Computation-guided experimentation reduces experimental workload by over 50%, shortens Class III medical device registration cycles by more than one year, and has supported multiple products in advancing to clinical translation.

Integrating deep learning with multi-scale biophysical simulations, the platform enables de novo generation of high-activity antibodies directly from antigen epitopes, addressing undruggable targets and navigating patent barriers.

The discovery cycle is reduced from 2–3 years to 6–12 months, with R&D costs lowered by 70%, while maintaining high prediction accuracy even for novel targets with no prior data.

Integrating AI with bioinformatics algorithms, the platform rapidly identifies potential targets amenable to small nucleic acid interference and intelligently designs candidate sequences—including siRNA and ASO—with precise prediction of silencing efficacy and off-target risks, enabling highly specific optimization.

Multi-dimensional delivery strategy assessments are provided for diverse tissues and organs, including liver and CNS, accelerating the clinical translation of candidate molecules.

Integrating AI with molecular dynamics simulations, the platform enables intelligent structural design and functional prediction of enzyme proteins, rapidly optimizing catalytic activity, thermostability, and substrate specificity—broadening the enzymatic properties and application boundaries.

Precise prediction of degradation risks under in vitro conditions guides mutagenesis and process optimization, accelerating the industrial-scale application of enzyme preparations.

Backed by tens of millions of real experimental data points—covering multidimensional parameters including imaging, morphology, flow cytometry, protein expression, and expansion kinetics—and encompassing a full spectrum of cell therapy products such as MSC, TSPC, iPSC, NK, and CAR-T, the platform continuously feeds data back into algorithm refinement.

With deep involvement in multiple industry standard-setting initiatives, the platform has reduced R&D timelines by 40% and doubled success rates, transforming cell therapy development from experience-based trial-and-error to data-driven precision.