Neoantigens represent highly promising targets in tumor immunotherapy, distinguished by their high specificity and immunogenicity. These unique antigens can elicit robust tumor-specific T cell responses, offering new therapeutic avenues for cancer treatment.
Yet identifying neoantigens capable of triggering potent T cell immunity remains a formidable challenge—akin to finding a needle in a haystack. This process involves deciphering the complex interactions among three highly polymorphic biological sequences: T cell receptors (TCRs), antigens, and human leukocyte antigens (HLAs). From vast combinations of these sequences, only a small subset forms functional TCR–antigen–HLA ternary complexes. Furthermore, developing predictive models that are both physically interpretable and data-efficient is essential for advancing our understanding of immune responses. Complicating matters, biological sequence variability across individuals poses a key challenge: how can models capture the intrinsic patterns of residue interactions and generalize to rare or unseen sequences?
Recent advances in artificial intelligence have made rapid identification and prediction of tumor neoantigens increasingly feasible. Building on Yancheng Bio’s extensive, experimentally validated databases of neoantigen-positive and -negative sequences, as well as shared neoantigen datasets, we have partnered closely with Tencent (HKEX: 00700) to establish the Tumor Neoantigen Analysis AI Prediction Platform. This platform harnesses the strengths of interdisciplinary collaboration—spanning pharmaceutical sciences, AI, clinical medicine, and computational biology—to integrate data analytics and develop novel algorithms. Through machine learning–driven tools, the platform enables precise screening of tumor neoantigens, accurate prediction of TCR–antigen–HLA binding interactions, and visualization of amino acid-level binding dynamics.
The Tumor Neoantigen Analysis AI Prediction Platform serves as a foundational pillar of our immuno-oncology pipeline. By leveraging AI to enhance the precision of neoantigen identification and immunogenicity screening, we aim to reduce development risk and accelerate the advancement of cancer vaccines, adoptive cell therapies, antibody-based therapeutics, and other immunotherapeutic modalities.