Contact

  • Academic
    dhy@stanford.edu
    University - Student Department: Chemical Engineering Position: Graduate

All Publications

  • Efficient generation of epitope-targeted antibodies with Germinal. Nature biotechnology Mille-Fragoso, L. S., Driscoll, C. L., Wang, J. N., Dai, H., Widatalla, T., Zhang, J. L., Zhang, X., Rao, B., Feng, L., Hie, B. L., Gao, X. J. 2026

    Abstract

    Obtaining antibodies to specific protein targets is a widely important yet experimentally laborious process. Meanwhile, computational methods for antibody design have been limited by low success rates that require resource-intensive screening. Here we introduce Germinal, a broadly enabling generative pipeline that designs antibodies against specific epitopes with nanomolar binding affinities while requiring only low-n experimental testing. Our method co-optimizes antibody structure and sequence by integrating a structure predictor with an antibody-specific protein language model to perform de novo design of functional complementarity-determining regions onto a user-specified structural framework. When tested against four diverse protein targets, Germinal designed functional antibodies across all targets and binder formats, testing only 43-101 designs for each antigen. Validated designs also exhibited robust expression in mammalian cells and high sequence and structural novelty. We provide open-source code and full computational and experimental protocols to facilitate wide adoption.

    View details for DOI 10.1038/s41587-026-03187-0

    View details for PubMedID 42337361

    View details for PubMedCentralID 431171

  • Genome modelling and design across all domains of life with Evo 2. Nature Brixi, G., Durrant, M. G., Ku, J., Naghipourfar, M., Poli, M., Sun, G., Brockman, G., Chang, D., Fanton, A., Gonzalez, G. A., King, S. H., Li, D. B., Merchant, A. T., Nguyen, E., Ricci-Tam, C., Romero, D. W., Schmok, J. C., Taghibakhshi, A., Vorontsov, A., Yang, B., Deng, M., Gorton, L., Nguyen, N., Wang, N. K., Pearce, M. T., Simon, E., Adams, E., Amador, Z. J., Ashley, E. A., Baccus, S. A., Dai, H., Dillmann, S., Ermon, S., Guo, D., Herschl, M. H., Ilango, R., Janik, K., Lu, A. X., Mehta, R., Mofrad, M. R., Ng, M. Y., Pannu, J., Ré, C., St John, J., Sullivan, J., Tey, J., Viggiano, B., Zhu, K., Zynda, G., Balsam, D., Collison, P., Costa, A. B., Hernandez-Boussard, T., Ho, E., Liu, M. Y., McGrath, T., Powell, K., Pinglay, S., Burke, D. P., Goodarzi, H., Hsu, P. D., Hie, B. L. 2026

    Abstract

    All of life encodes information with DNA. Although tools for genome sequencing, synthesis and editing have transformed biological research, we still lack sufficient understanding of the immense complexity encoded by genomes to predict the effects of many classes of genomic changes or to intelligently compose new biological systems. Artificial intelligence models that learn information from genomic sequences across diverse organisms have increasingly advanced prediction and design capabilities1,2. Here we introduce Evo 2, a biological foundation model trained on 9 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life to have a 1 million token context window with single-nucleotide resolution. Evo 2 learns to accurately predict the functional impacts of genetic variation-from noncoding pathogenic mutations to clinically significant BRCA1 variants-without task-specific fine-tuning. Mechanistic interpretability analyses reveal that Evo 2 learns representations associated with biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements and prophage genomic regions. The generative abilities of Evo 2 produce mitochondrial, prokaryotic and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Evo 2 also generates experimentally validated chromatin accessibility patterns when guided by predictive models3,4 and inference-time search. We have made Evo 2 fully open, including model parameters, training code5, inference code and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

    View details for DOI 10.1038/s41586-026-10176-5

    View details for PubMedID 41781614

    View details for PubMedCentralID 12057570

  • Efficient generation of epitope-targetedde novoantibodies with Germinal. bioRxiv : the preprint server for biology Mille-Fragoso, L. S., Wang, J. N., Driscoll, C. L., Dai, H., Widatalla, T., Zhang, X., Hie, B. L., Gao, X. J. 2025

    Abstract

    Obtaining novel antibodies against specific protein targets is a widely important yet experimentally laborious process. Meanwhile, computational methods for antibody design have been limited by low success rates that currently require resource-intensive screening. Here, we introduce Germinal, a broadly enabling generative framework that designs antibodies against specific epitopes with nanomolar binding affinities while requiring only low-n experimental testing. Our method co-optimizes antibody structure and sequence by integrating a structure predictor with an antibody-specific protein language model to perform de novo design of functional complementarity-determining regions (CDRs) onto a user-specified structural framework. When tested against four diverse protein targets, Germinal achieved an experimental success rate of 4-22% across all targets, testing only 43-101 designs for each antigen. Validated nanobodies also exhibited robust expression in mammalian cells and nanomolar binding affinities. We provide open-source code and full computational and experimental protocols to facilitate wide adoption. Germinal represents a milestone in efficient, epitope-targeted de novo antibody design, with notable implications for the development of molecular tools and therapeutics.

    View details for DOI 10.1101/2025.09.19.677421

    View details for PubMedID 41040335