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arxiv: 2604.24506 · v1 · submitted 2026-04-27 · 💻 cs.AI · cs.LG

Recognition: unknown

MIMIC: A Generative Multimodal Foundation Model for Biomolecules

Siavash Golkar , Jake Kovalic , Irina Espejo Morales , Samuel Sledzieski , Minhuan Li , Ksenia Sokolova , Geraud Krawezik , Alberto Bietti
show 23 more authors
Claudia Skok Gibbs Roman Klypa Shengwei Xiong Francois Lanusse Liam Parker Kyunghyun Cho Miles Cranmer Tom Hehir Michael McCabe Lucas Meyer Rudy Morel Payel Mukhopadhyay Mariel Pettee Helen Qu Jeff Shen David Fouhey Hadi Sotoudeh Vikram Mulligan Pilar Cossio Sonya M. Hanson Alisha N. Jones Olga G. Troyanskaya Shirley Ho
Authors on Pith no claims yet

Pith reviewed 2026-05-08 03:27 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords multimodal foundation modelbiomoleculesgenerative modelRNA splicingprotein designbiomolecular reconstructionLORE datasetconditional generation
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The pith

A generative model conditions on any mix of sequence, structure, evolution and regulation to reconstruct and design biomolecules.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents MIMIC as a foundation model trained on aligned data across nucleic acids, proteins, evolution, structure, regulation and context. It uses a split-track encoder-decoder to handle partial observations of molecular states and to generate or reconstruct the missing parts. Multimodal conditioning improves sequence reconstruction over sequence-only baselines, while the learned representations support state-of-the-art results on RNA and protein tasks including splicing prediction. The same generative setup enables isoform-aware inference and constrained design examples such as corrective RNA edits and protein sequences with target-binding properties. A sympathetic reader would care because the work proposes a single framework that moves from data integration to both accurate prediction and practical molecular engineering.

Core claim

MIMIC is a generative multimodal foundation model trained on the LORE dataset linking nucleic acid, protein, evolutionary, structural, regulatory and semantic modalities within partially observed biomolecular states. The split-track encoder-decoder conditions on arbitrary subsets of observed modalities to reconstruct or generate missing components across genome, transcriptome and proteome. Multimodal conditioning improves sequence reconstruction relative to sequence-only inputs, learned representations enable state-of-the-art performance on RNA and protein downstream tasks, and the model reaches state-of-the-art splicing prediction with further gains from isoform-aware inference. The joint生成

What carries the argument

The split-track encoder-decoder architecture that processes modalities in separate tracks while sharing a joint generative space to condition on partial observations and produce complete molecular states.

If this is right

  • Multimodal conditioning improves sequence reconstruction accuracy relative to sequence-only inputs.
  • Learned representations enable state-of-the-art performance on RNA and protein downstream tasks.
  • The model achieves state-of-the-art splicing prediction.
  • Isoform-aware inference further improves performance on relevant tasks.
  • The generative framework supports constrained design such as corrective RNA edits and protein sequences with target binding.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the alignments learned on LORE transfer to new biomolecules or modalities, the model could support design in areas with sparse data.
  • The semantic conditioning mechanism might be extended to model how experimental conditions alter molecular behavior in living cells.
  • Joint modeling of evolutionary and structural signals could be tested for designing molecules that remain functional under mutation pressure.
  • Applying the same split-track approach to multi-molecule complexes rather than single sequences would be a direct next test.

Load-bearing premise

The newly curated LORE dataset supplies accurate and representative alignments across nucleic acid, protein, evolutionary, structural, regulatory and semantic modalities despite many partial observations.

What would settle it

Training MIMIC on LORE and finding no consistent improvement in sequence reconstruction accuracy or downstream task performance when adding multimodal conditioning compared with sequence-only inputs on held-out test data would falsify the central claims.

Figures

Figures reproduced from arXiv: 2604.24506 by Alberto Bietti, Alisha N. Jones, Claudia Skok Gibbs, David Fouhey, Francois Lanusse, Geraud Krawezik, Hadi Sotoudeh, Helen Qu, Irina Espejo Morales, Jake Kovalic, Jeff Shen, Ksenia Sokolova, Kyunghyun Cho, Liam Parker, Lucas Meyer, Mariel Pettee, Michael McCabe, Miles Cranmer, Minhuan Li, Olga G. Troyanskaya, Payel Mukhopadhyay, Pilar Cossio, Roman Klypa, Rudy Morel, Samuel Sledzieski, Shengwei Xiong, Shirley Ho, Siavash Golkar, Sonya M. Hanson, Tom Hehir, Vikram Mulligan.

Figure 1
Figure 1. Figure 1: Overview of the MIMIC framework. (A) Molecular biology data is highly heterogeneous, spanning genomic, transcriptomic, and proteomic sequences, each with multiple assays and measurements that describe their function. (B) We curate LORE, a multimodal dataset that integrates and aligns data from multiple repositories into a set of unified but partially observed training examples. (C) Using this data, we buil… view at source ↗
Figure 2
Figure 2. Figure 2: MIMIC achieves state-of-the-art performance across RNA and protein sequence property prediction benchmarks. (A) Per-residue top-1 amino acid inpainting accuracy at 100 masked positions. MIMIC (with structural and surface conditioning) outperforms all sequence-only protein language model baselines including ESM3-open, ESM-C, ESM-2 (650M), and ProtBERT. (B-C) Per-nucleotide top-1 inpainting accuracy at 100 m… view at source ↗
Figure 3
Figure 3. Figure 3: MIMIC accurately predicts splice sites and designs RNA sequences with predictable splice patterns. (A) Gene-level splice site prediction: MIMIC takes a genomic region as input and predicts donor and acceptor positions. Across coding (left) and non-coding (right) regions, MIMIC outperforms AlphaGenome, SpliceAI, and NTv3. (B) Transcript-conditioned splice prediction: providing transcript context in terms of… view at source ↗
Figure 4
Figure 4. Figure 4: MIMIC designs recover target-binder properties with high sequence diversity. (A) Schematic of the target binding complex (binder in blue, receptor in grey, binding site in red), use SARS-Cov-2-RBD - hACE2 (PDB ID: 6VW1) as an example. (B) Overview of the MIMIC design pipeline. The model generates novel sequences conditioned on the wild-type (WT) binder’s backbone coordinates, MaSIF surface fingerprints, or… view at source ↗
Figure 5
Figure 5. Figure 5: MIMIC leverages experimental context for accurate RNA reactivity prediction and RNA structure modeling. (A-B) MIMIC accurately predicts transcriptome-wide chemical probing reactivity (RASP2 scores) and adapts to condition-specific contexts. (A) Pearson correlation coefficients (r) between MIMIC-predicted and experimentally measured RASP2 scores for coding and non-coding RNAs. The context-aware generation (… view at source ↗
read the original abstract

Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained within one modality or for a fixed forward task. We present MIMIC, a generative multimodal foundation model trained on our newly curated and aligned dataset, LORE, linking nucleic acid, protein, evolutionary, structural, regulatory, and semantic/contextual modalities within partially observed biomolecular states. MIMIC uses a split-track encoder-decoder architecture to condition on arbitrary subsets of observed modalities and reconstruct or generate missing components of molecular state across the genome, transcriptome, and proteome. Multimodal conditioning consistently improves MIMIC's sequence reconstruction relative to sequence-only inputs, while its learned representations enable state-of-the-art performance on RNA and protein downstream tasks. MIMIC achieves state-of-the-art splicing prediction, and its joint generative formulation enables isoform-aware inference that further improves performance. Beyond prediction, the same generative framework supports constrained design. For RNA, MIMIC identifies corrective edits in a clinically relevant HBB splice-disrupting mutation without reverting it by using evolutionary and structural signals. For proteins, jointly conditioning on shape and surface chemistry of PD-L1 and hACE2 binding sites produces diverse, high-confidence sequences with strong in silico support for target binding. Finally, MIMIC uses experimental context as semantic conditioning to model assay-dependent RNA chemical probing, rather than treating context as a fixed output. Together, these results position MIMIC's aligned multimodal generative modeling as a strong foundation for unifying representation learning, conditional prediction, and constrained biomolecular design within a single model.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. MIMIC is a generative multimodal foundation model for biomolecules trained on the newly curated LORE dataset aligning nucleic acid, protein, evolutionary, structural, regulatory, and semantic modalities under partial observations. It employs a split-track encoder-decoder architecture to condition on arbitrary modality subsets for reconstruction or generation across genome, transcriptome, and proteome. The paper claims multimodal conditioning yields consistent improvements in sequence reconstruction over sequence-only baselines, enables SOTA performance on RNA/protein downstream tasks including splicing prediction, supports isoform-aware inference, and facilitates constrained design tasks such as corrective RNA edits and protein binding sequence generation, plus context-dependent probing modeling.

Significance. If validated with quantitative evidence, the work would advance biological foundation models by providing a unified generative framework that integrates coupled constraints across modalities, potentially improving both predictive tasks and constrained design beyond single-modality approaches. The split-track handling of partial observations and isoform-aware generation represent technical strengths that could influence future multimodal modeling in genomics and proteomics.

major comments (2)
  1. [LORE dataset] LORE dataset section: The manuscript provides no details on alignment procedures, accuracy metrics, error rates, or controls for systematic biases from partial observations across modalities. This is load-bearing for the central claim that the split-track architecture learns useful joint representations enabling multimodal gains, as the abstract's performance assertions rest entirely on this unverified curation.
  2. [Abstract and results] Abstract and results summary: Claims of 'state-of-the-art splicing prediction', 'consistent improvement' in reconstruction, and 'further improves performance' via isoform-aware inference are stated without quantitative metrics, error bars, ablation studies, baseline comparisons, or dataset statistics. This prevents verification of effect sizes and undermines assessment of whether gains derive from the generative formulation or dataset artifacts.
minor comments (1)
  1. [Methods] The split-track architecture would benefit from an explicit diagram or pseudocode in the methods to clarify conditioning on arbitrary modality subsets.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review, which has helped us strengthen the clarity and verifiability of our work. We have revised the manuscript to address both major comments by expanding the LORE dataset description and incorporating quantitative metrics, ablations, and comparisons throughout the abstract and results. Our point-by-point responses follow.

read point-by-point responses

  1. Referee: [LORE dataset] LORE dataset section: The manuscript provides no details on alignment procedures, accuracy metrics, error rates, or controls for systematic biases from partial observations across modalities. This is load-bearing for the central claim that the split-track architecture learns useful joint representations enabling multimodal gains, as the abstract's performance assertions rest entirely on this unverified curation.

    Authors: We agree that detailed documentation of the LORE dataset curation is essential to substantiate the multimodal gains and the role of the split-track architecture. In the revised manuscript we have added an expanded LORE dataset section that describes the alignment procedures (cross-referencing via stable identifiers from RefSeq, UniProt, ENCODE, and GTEx), reports accuracy metrics and error rates obtained from a held-out validation set against independent annotations, and includes controls for systematic biases (distributional comparisons, sensitivity analyses under varying partial-observation rates, and checks for annotation-source imbalances). These additions directly support that the observed improvements arise from joint representation learning rather than curation artifacts. revision: yes

  2. Referee: [Abstract and results] Abstract and results summary: Claims of 'state-of-the-art splicing prediction', 'consistent improvement' in reconstruction, and 'further improves performance' via isoform-aware inference are stated without quantitative metrics, error bars, ablation studies, baseline comparisons, or dataset statistics. This prevents verification of effect sizes and undermines assessment of whether gains derive from the generative formulation or dataset artifacts.

    Authors: We acknowledge that the original abstract and results summary presented claims qualitatively. We have revised both the abstract and the main results section to include the requested quantitative information: specific AUROC and accuracy values for splicing prediction with direct comparisons to prior SOTA methods, percentage improvements in sequence reconstruction together with standard deviations across multiple runs, ablation tables isolating each modality and the isoform-aware component, baseline comparisons on the same datasets, and summary statistics of the LORE splits. These additions allow verification of effect sizes and confirm that the gains are attributable to the multimodal generative formulation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on external benchmarks and held-out evaluations rather than self-referential inputs.

full rationale

The paper's core claims concern empirical improvements from multimodal conditioning on the LORE dataset and SOTA results on RNA/protein downstream tasks. No equations, derivations, or self-citations are presented that reduce any prediction to its own fitted inputs by construction. The split-track architecture and generative formulation are described as standard encoder-decoder components conditioned on observed modalities, with performance gains asserted via comparisons to sequence-only baselines and prior models. These evaluations are positioned against external tasks and benchmarks, rendering the work self-contained. While the newly curated LORE dataset introduces a potential verification gap regarding alignment accuracy, this is an empirical concern rather than a circular reduction in the derivation chain. No self-definitional, fitted-input-renamed-as-prediction, or load-bearing self-citation patterns are identifiable from the provided text.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 1 invented entities

The central claims rest on the existence and quality of the LORE dataset and on the assumption that the split-track architecture can learn transferable representations from partially observed multimodal states without additional regularization or inductive biases.

free parameters (2)
  • LORE dataset alignment parameters
    Curated alignments across modalities are treated as ground truth; any fitting or filtering choices during curation act as free parameters.
  • Model hyperparameters
    Standard transformer-scale hyperparameters (layers, heads, embedding size) are not specified and must be tuned.
axioms (1)
  • domain assumption Partially observed biomolecular states can be reconstructed or generated from arbitrary subsets of modalities using a shared latent space.
    Invoked in the description of the split-track encoder-decoder and the claim that multimodal conditioning improves reconstruction.
invented entities (1)
  • LORE dataset no independent evidence
    purpose: Aligned multimodal training corpus linking sequence, structure, evolution, regulation, and context.
    Newly curated resource introduced to support the multimodal training; no independent evidence of its construction or quality is provided.

pith-pipeline@v0.9.0 · 5723 in / 1476 out tokens · 44994 ms · 2026-05-08T03:27:27.232180+00:00 · methodology

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