Contact

  • Academic
    adititm@stanford.edu
    University - Student Department: Bioengineering Position: Graduate

All Publications

  • Generative design of bacteriophages with genome language models. Science (New York, N.Y.) King, S. H., Driscoll, C. L., Li, D. B., Guo, D., Merchant, A. T., Brixi, G., Wilkinson, M. E., Hie, B. L. 2026; 393 (6811): eaec2657

    Abstract

    Many important biological functions arise not from single genes but from complex interactions encoded by entire genomes. We report the first generative design of complete bacteriophage genomes using genome language models. We generated viable bacteriophages with target host tropism, using the phage ΦX174 as our design template. Experimental testing yielded 16 phages with diverse fitness profiles in laboratory conditions. Cryo-electron microscopy confirmed that a generated phage utilizes an evolutionarily distant DNA packaging protein in its capsid. A cocktail of generated phages rapidly overcomes ΦX174-resistant Escherichia coli strains, demonstrating a path toward artificial intelligence-generated phage therapies against rapidly evolving bacterial pathogens. This work provides a blueprint for the design of diverse synthetic bacteriophages and useful biological systems at the genome scale.

    View details for DOI 10.1126/science.aec2657

    View details for PubMedID 42561074

  • 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

  • Semantic design of functional de novo genes from a genomic language model. Nature Merchant, A. T., King, S. H., Nguyen, E., Hie, B. L. 2025

    Abstract

    Generative genomic models can design increasingly complex biological systems1. However, controlling these models to generate novel sequences with desired functions remains challenging. Here, we show that Evo, a genomic language model, can leverage genomic context to perform function-guided design that accesses novel regions of sequence space. By learning semantic relationships across prokaryotic genes2, Evo enables a genomic 'autocomplete' in which a DNA prompt encoding genomic context for a function of interest guides the generation of novel sequences enriched for related functions, which we refer to as 'semantic design'. We validate this approach by experimentally testing the activity of generated anti-CRISPR proteins and type II and III toxin-antitoxin systems, including de novo genes with no significant sequence similarity to natural proteins. In-context design of proteins and non-coding RNAs with Evo achieves robust activity and high experimental success rates even in the absence of structural priors, known evolutionary conservation or task-specific fine-tuning. We then use Evo to complete millions of prompts to produce SynGenome, a database containing over 120 billion base pairs of artificial intelligence-generated genomic sequences that enables semantic design across many functions. More broadly, these results demonstrate that generative genomics with biological language models can extend beyond natural sequences.

    View details for DOI 10.1038/s41586-025-09749-7

    View details for PubMedID 41261132

    View details for PubMedCentralID 12057570