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<!DOCTYPE html>
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<meta name="description"
content="A unified survey of generative AI methods for biomolecular modeling, covering language models, diffusion models, and multi-modal foundation models.">
<meta name="keywords" content="Generative AI, Biomolecule, Language Models, Diffusion Models, Protein Design, Foundation Models, Survey">
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<title>Generative AI for Biology</title>
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<h1 class="title is-2 publication-title">Generative Artificial Intelligence for Biology:<br>Toward Unifying Models, Algorithms, and Modalities</h1>
<div class="is-size-6 publication-authors">
<span class="author-block">Xiner Li<sup>1,7,*,†</sup>,</span>
<span class="author-block">Xingyu Su<sup>1,*</sup>,</span>
<span class="author-block">Yuchao Lin<sup>1,11</sup>,</span>
<span class="author-block">Chenyu Wang<sup>2</sup>,</span>
<span class="author-block">Yijia Xiao<sup>3</sup>,</span>
<span class="author-block">Tianyu Liu<sup>4</sup>,</span>
<span class="author-block">Chi Han<sup>5</sup>,</span>
<span class="author-block">Michael Sun<sup>2</sup>,</span>
<span class="author-block">Montgomery Bohde<sup>1</sup>,</span>
<span class="author-block">Anna Hart<sup>5</sup>,</span>
<span class="author-block">Wendi Yu<sup>1</sup>,</span>
<span class="author-block">Masatoshi Uehara<sup>6</sup>,</span>
<span class="author-block">Gabriele Scalia<sup>7</sup>,</span>
<span class="author-block">Xiao Luo<sup>8</sup>,</span>
<span class="author-block">Carl Edwards<sup>7</sup>,</span>
<span class="author-block">Wengong Jin<sup>9,10</sup>,</span>
<span class="author-block">Jianwen Xie<sup>11</sup>,</span>
<span class="author-block">Ehsan Hajiramezanali<sup>7</sup>,</span>
<span class="author-block">Edward De Brouwer<sup>7</sup>,</span>
<span class="author-block">Qing Sun<sup>12</sup>,</span>
<span class="author-block">Byung-Jun Yoon<sup>13,16</sup>,</span>
<span class="author-block">Xiaoning Qian<sup>1,13,16</sup>,</span>
<span class="author-block">Marinka Zitnik<sup>14</sup>,</span>
<span class="author-block">Heng Ji<sup>5</sup>,</span>
<span class="author-block">Hongyu Zhao<sup>4</sup>,</span>
<span class="author-block">Wei Wang<sup>3</sup>,</span>
<span class="author-block">Shuiwang Ji<sup>1,15,17,†</sup></span>
</div>
<div class="is-size-7 publication-authors" style="margin-top: 0.5rem;">
<span class="author-block"><sup>1</sup>Department of Computer Science and Engineering, Texas A&M University,</span>
<span class="author-block"><sup>2</sup>MIT CSAIL,</span>
<span class="author-block"><sup>3</sup>Department of Computer Science, UCLA,</span>
<span class="author-block"><sup>4</sup>Interdepartmental Program of Computational Biology and Bioinformatics, Yale University,</span>
<span class="author-block"><sup>5</sup>Siebel School of Computing and Data Science, UIUC,</span>
<span class="author-block"><sup>6</sup>Chan Zuckerberg Initiative,</span>
<span class="author-block"><sup>7</sup>Genentech, Inc.,</span>
<span class="author-block"><sup>8</sup>Department of Statistics, University of Wisconsin–Madison,</span>
<span class="author-block"><sup>9</sup>Broad Institute of MIT and Harvard,</span>
<span class="author-block"><sup>10</sup>Khoury College of Computer Sciences, Northeastern University,</span>
<span class="author-block"><sup>11</sup>Lambda, Inc.,</span>
<span class="author-block"><sup>12</sup>Artie McFerrin Department of Chemical Engineering, Texas A&M University,</span>
<span class="author-block"><sup>13</sup>Department of Electrical and Computer Engineering, Texas A&M University,</span>
<span class="author-block"><sup>14</sup>Department of Biomedical Informatics, Harvard Medical School,</span>
<span class="author-block"><sup>15</sup>Department of Materials Science and Engineering, Texas A&M University,</span>
<span class="author-block"><sup>16</sup>Computing and Data Sciences, Brookhaven National Laboratory,</span>
<span class="author-block"><sup>17</sup>J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University</span>
</div>
<div class="is-size-7 publication-authors" style="margin-top: 0.3rem;">
<span class="author-block"><sup>*</sup>Equal Contribution,</span>
<span class="author-block"><sup>†</sup>Correspondence to: Xiner Li <lxe@tamu.edu>, Shuiwang Ji <sji@tamu.edu></span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
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class="external-link button is-normal is-rounded is-dark">
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</div>
</section>
<section class="hero teaser">
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<img src="./static/images/fig1.png"
alt="Layered taxonomy of generative AI for biomolecule"
style="width: 100%; border-radius: 5px;">
<h2 class="subtitle has-text-centered" style="margin-top: 1rem;">
<b>Figure 1.</b> A layered taxonomy of generative AI for biology.
Our perspective organizes the field from biological problem spaces and core architectural building blocks, through major generative paradigms such as autoregressive, masked/bidirectional, and diffusion/flow matching, to unified cross-paradigm hybrids, multimodal biological foundation models, and tool-augmented agentic systems for closed-loop discovery.
</h2>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Rapid advances in generative artificial intelligence have revolutionized biological modeling across domains such as protein, genetics, and single-cell.
However, existing works often organize applications by molecule types or specific research tasks, overlooking the methodological convergence and cross-modal innovations.
This paper aims to present a unified methodological perspective that highlights the fundamental technical commonalities across biological modalities.
We systematically organize recent advances in generative modeling for biology through the lens of core machine learning paradigms, from language models (LMs) and diffusion models to their emerging hybrid architectures.
Our work reveals how techniques initially developed for one molecular type (e.g., protein design) can be effectively transferred to others (e.g., RNA engineering), and identifies the convergence trend where discrete diffusion models and iterative language models represent different facets of a unified generative framework.
We cover the evolution from domain-specific models to multi-modal biological foundation models and agent-based systems.
By emphasizing methodological connections rather than applications, this paper aims to accelerate cross-domain innovation and make the field more accessible to the broader machine learning community.
We conclude by identifying promising research directions where successful techniques in one biological domain remain unexplored in others, offering a roadmap for future advances in generative biology.
</p>
</div>
</div>
</div>
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</section>
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<h2 class="title is-3">Survey Overview</h2>
</div>
</div>
<!-- Figure 2: Language Models -->
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<div class="column is-full-width">
<h3 class="title is-4">Language Modeling for Biological Generation</h3>
<div class="paper-figure">
<img src="./static/images/fig2.png"
alt="Language-modeling pipelines for biomolecular generation">
<p class="figure-caption">
<b>Figure 2.</b> An overview of language-modeling pipelines for biological generation, illustrating how diverse biological inputs are tokenized, processed by a Transformer backbone, and trained under either masked bidirectional-context modeling or autoregressive causal modeling objectives.
</p>
</div>
</div>
</div>
<!-- Figure 3: Diffusion Models -->
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<h3 class="title is-4">Diffusion & Flow Matching Models</h3>
<div class="paper-figure">
<img src="./static/images/fig3.png"
alt="Forward-reverse diffusion pipeline for biomolecular generation">
<p class="figure-caption">
<b>Figure 3.</b> Overview of the forward-reverse diffusion pipeline for biological generation, showing the forward noising process and the learned reverse denoising process, in both discrete (token-level) and continuous (geometry-level) formulations.
</p>
</div>
</div>
</div>
<!-- Figure 4: Unified View -->
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<h3 class="title is-4">Toward Unified Models</h3>
<div class="paper-figure">
<img src="./static/images/fig4.png"
alt="Unified generative-model view">
<p class="figure-caption">
<b>Figure 4.</b> A unified generative-model view integrating classic architectures, language models, and diffusion models. This illustrates the perspective that iterative language modeling can be viewed as tokenized diffusion, where training and inference correspond to iterative denoising, supported by shared backbones and modality-specific (discrete/continuous) output heads.
</p>
</div>
</div>
</div>
<!-- Figure 5: Multi-Modal Foundation Models -->
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<div class="column is-full-width">
<h3 class="title is-4">Multi-Modal Foundation Models</h3>
<div class="paper-figure">
<img src="./static/images/fig5.png"
alt="Multi-modal foundation model for biomolecules">
<p class="figure-caption">
<b>Figure 5.</b> A multi-modal foundation model for biomolecules, where heterogeneous inputs are embedded and fused into a backbone to support diverse downstream design tasks such as binder/vaccine design, motif scaffolding, structure generation, and inverse folding.
</p>
</div>
</div>
</div>
<!-- Figure 6: Biological Agent Systems -->
<div class="columns is-centered">
<div class="column is-full-width">
<h3 class="title is-4">Biological Agent Systems</h3>
<div class="paper-figure">
<img src="./static/images/fig6.png"
alt="Biological agent paradigm">
<p class="figure-caption">
<b>Figure 6.</b> The biological agent paradigm that couples hypothesis generation with tool use/creation and laboratory automation, enabling iterative refinement from computational reasoning to experimental validation.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- Original demo sections commented out - preserved for potential future use -->
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<h2 class="title is-3">Visual Effects</h2>
<p>
Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
would be impossible without nerfies since it would require going through a wall.
</p>
<video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
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As a byproduct of our method, we can also solve the matting problem by ignoring
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<h3 class="title is-4">Interpolating states</h3>
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<p>
We can also animate the scene by interpolating the deformation latent codes of two input
frames. Use the slider here to linearly interpolate between the left frame and the right
frame.
</p>
</div>
<div class="columns is-vcentered interpolation-panel">
<div class="column is-3 has-text-centered">
<img src="./static/images/interpolate_start.jpg"
class="interpolation-image"
alt="Interpolate start reference image."/>
<p>Start Frame</p>
</div>
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<div id="interpolation-image-wrapper">
Loading...
</div>
<input class="slider is-fullwidth is-large is-info"
id="interpolation-slider"
step="1" min="0" max="100" value="0" type="range">
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<img src="./static/images/interpolate_end.jpg"
class="interpolation-image"
alt="Interpolation end reference image."/>
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</div>
</div>
<br/>
<h3 class="title is-4">Re-rendering the input video</h3>
<div class="content has-text-justified">
<p>
Using nerfies, you can re-render a video from a novel
viewpoint such as a stabilized camera by playing back the training deformations.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
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<h2 class="title is-3">Related Links</h2>
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<p>
There's a lot of excellent work that was introduced around the same time as ours.
</p>
<p>
<a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
</p>
<p>
<a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
both use deformation fields to model non-rigid scenes.
</p>
<p>
Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
</p>
<p>
There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{
GenAI4bio:Li,
author = {Xiner Li and Xingyu Su and Yuchao Lin and Chenyu Wang and Yijia Xiao and Tianyu Liu and Chi Han and Michael Sun and Montgomery Bohde and Anna Hart and Wendi Yu and Masatoshi Uehara and Gabriele Scalia and Xiao Luo and Carl Edwards and Wengong Jin and Jianwen Xie and Ehsan Hajiramezanali and Edward De Brouwer and Qing Sun and Byung-Jun Yoon and Xiaoning Qian and Marinka Zitnik and Heng Ji and Hongyu Zhao and Wei Wang and Shuiwang Ji},
title = {Generative Artificial Intelligence for Biology: Toward Unifying Models, Algorithms, and Modalities},
journal = {ChemRxiv},
volume = {2026},
number = {0212},
pages = {},
year = {2026},
}</code></pre>
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