ESM-IF: Inverse Folding & Multi-Domain Fusion

• ESM-IF is a multi-domain framework that includes inverse folding models for structure-aware protein and antibody design, integrating 3D geometric priors via graph-based encoders.
• SimBinder-IF refines the baseline by using preference-based fine-tuning on the autoregressive decoder to enhance binding affinity predictions, achieving significant benchmarking gains.
• Additional ESM-IF variants apply to sensor fusion and Earth system emulation, employing probabilistic methods and score-based diffusion models to improve classification and climate impact forecasts.

ESM-IF refers to a set of frameworks and models that share the acronym but originate in distinct domains, including protein engineering, sensor fusion in surveillance systems, and Earth system modeling. The most prominent contemporary reference is to the ESM-IF (Evolutionary Scale Modeling–Inverse Folding) inverse folding model, widely used for structure-aware protein and antibody design. Additionally, "ESM-IF" appears as an abbreviation for "ESM–Impact-Focused" earth system model emulation and as the name for an "ESM–Information Fusion" framework in multi-sensor recognition contexts. Each instance is characterized by purpose-specific architectures, mathematical formulations, and operational domains.

1. Structure-Aware Protein Design: ESM-IF in Antibody Generation

The ESM-IF model is fundamentally an inverse folding architecture. Given the three-dimensional atomic coordinates of a protein complex—most notably antibody–antigen complexes—its goal is to generate amino acid sequences that are likely to fold into the supplied structure. ESM-IF is built upon the following components (Zhao et al., 19 Dec 2025):

• Structure Encoder: Inputs the 3D backbone atomic coordinates $X = \{x_1,\dots,x_{3n}\}$ with $n$ residues and maps them into per-residue feature embeddings $E = f_{enc}(X) \in \mathbb{R}^{n \times d}$ using a graph-based Geometric Vector Perceptron Graph Neural Network (GVP-GNN).
• Autoregressive Decoder: At each step $i$, previously generated sequence tokens $y_{<i}$ are processed into embeddings $T_{<i}$, and the decoder attends to both $E$ and $T_{<i}$ to yield a hidden state $h_i$ and a categorical distribution over amino acids $p_\theta(y_i|y_{<i},X)$.
• Training Objective: During pre-training, the negative log-likelihood loss is minimized over large corpora of (structure, sequence) pairs:

$$\mathcal{L}_{NLL}(\theta) = -\sum_{(X,Y)} \log P_\theta(Y|X)$$

with sequence log-probabilities factorized autoregressively.

The ESM-IF design enables the generation or scoring of sequences structurally compatible with an input fold, supporting both natural and de novo protein design.

2. Affinity Optimization with SimBinder-IF and Preference-Based Fine-Tuning

Vanilla ESM-IF, while structure-aware, is not inherently aligned to protein function (e.g., antigen binding affinity). To address this, SimBinder-IF was introduced by fine-tuning ESM-IF through preference optimization (Zhao et al., 19 Dec 2025):

• Preference Optimization Setup:
  • SimBinder-IF utilizes pairwise training based on experimental data, presenting sequence pairs ($y_w$, $y_\ell$) where $y_w$ is a stronger binder than $y_\ell$.
  • The Simple Preference Optimization (SimPO) loss replaces explicit reference model scoring (as in Direct Preference Optimization) by using the model’s own length-normalized sequence log-likelihood:

  $$r_{SimPO}(x, y) = \frac{\beta}{|y|} \sum_{i=1}^{|y|} \log p_\theta(y_i|y_{<i},x)$$

  and enforces a fixed margin $\gamma$ between winners and losers in a Bradley–Terry logistic loss.

• Parameter-Efficient Training:

  • Only the autoregressive decoder layers (∼25M parameters, ≈18% of total) are updated, while the structure encoder and its Transformer encoder layers are frozen.
  • Training uses AdamW (learning rate $1 \times 10^{-4}$), batch size 32, for 3 epochs, with early stopping on validation Spearman correlation.

This approach exploits the rich geometric priors of the pre-trained structure encoder, avoiding catastrophic forgetting, while efficiently steering the decoder to prefer sequences with experimentally validated higher binding affinity.

3. Benchmarking and Quantitative Performance

Comprehensive evaluations using AbBiBench, a multi-assay antibody benchmark, and additional case studies quantify the practical impact of SimBinder-IF relative to vanilla ESM-IF (Zhao et al., 19 Dec 2025). Key results include:

Benchmark Condition	Vanilla ESM-IF ρ	SimBinder-IF ρ	Relative Gain
AbBiBench (Supervised)	0.264	0.410	+55%
Zero-shot (Unseen Complexes)	0.115	0.294	+156%

• Top-10 precision for $\geq 10$-fold affinity improvements: SimBinder-IF achieves substantial absolute gains (∼10–25%) over ESM-IF.
• Case Study—Redesign of F045-092 (pdmH1N1): SimBinder-IF variants yield mean predicted free energy changes $\Delta\Delta G = -75.16$ kcal/mol versus $-46.57$ for ESM-IF, indicating stronger predicted binding.

4. Methodological Rationale and Theoretical Insights

Analysis of SimBinder-IF demonstrates critical design choices:

• Freezing the Structure Encoder: Retains geometric priors and prevents forgetting of 3D context.
• Decoder-Only Fine-Tuning: Restricts affinity-based learning to sequence preferences, preserving general structural knowledge.
• Alignment of Training and Inference: SimPO’s reference-free objective eliminates discrepancies introduced by DPO’s dependence on a frozen reference, directly correlating log-likelihood with experimental affinity metrics.
• Regularization by Architectural Constraint: Limited trainable parameters prevent overfitting, especially given the large but finite set of labeled mutants for supervised preference updates.

These features drive measurable gains in sequence ranking by affinity and the practical generation of novel, high-affinity, and structurally plausible complementarity-determining region (CDR) loops.

5. "ESM-IF" in Sensor Fusion and Earth System Emulation

The ESM-IF designation also appears in multi-sensor information fusion and climate modeling:

• Sensor Fusion (ESM–IF): The fusion architecture for Electronic Support Measures and kinematic data (Taghavi et al., 2016) integrates raw ESM reports, attribute-based recognition, Bayesian and belief-function fusion, and sequential hypotheses filtering for target identification. Mathematical updates include attribute posteriors, IMM filtering for kinematic tracks, and Bayesian or Dempster–Shafer combination for final classifier outputs.

| Processing Block | Input | Output | |--------------------------|--------------------------------------|------------------------------------------| | ESM Measurement | Pulse vector $y$, noise covariance $U$ | Attribute vector and uncertainty | | Attribute Estimator | $\{y, U\}$ | Posterior probability over attributes | | Kinematic Tracker | Radar polar returns, variances | IMM-based state and mode likelihoods | | Data Association | ESM and radar-level information | Class posterior or belief mass fusion |

The architecture demonstrates significant gains, achieving up to 99% correct classification via full sensor-data fusion, with each processing block contributing statistical evidence for recognition.

• Earth System Model Emulation (ESM–Impact-Focused): ESM-IF also refers to a score-based generative emulator for impact-relevant climate model fields (Bouabid et al., 5 Oct 2025). Using HEALPix-based U-Nets and score-based diffusion conditioned on global temperature change, this emulator faithfully reproduces distributional properties, spatial structure, and trends of four major surface variables. The model offers rapid, distribution-preserving surrogates for ESM output, crucial for downstream impact modeling.

6. Limitations, Failure Modes, and Future Directions

Antibody Engineering (SimBinder-IF):

• The method's effectiveness depends on sufficient labeled variant sequence data. Overfitting is mitigated but could be exacerbated by smaller data sets.
• The current objective aligns sequence likelihoods only with affinity; it does not directly optimize for other therapeutic developability criteria.

Sensor Fusion ESM-IF:

• The fusion framework in (Taghavi et al., 2016) is limited by single-target, no-clutter simulations and assumes known class-conditional feature distributions. Extensions to multi-target and cluttered environments, as well as learning these distributions adaptively, remain open.

ESM-IF Emulation:

• Principal limitations are the underestimation of fine-scale variance and smoothing across regimes with bimodal or highly non-Gaussian seasonal distributions. Overfitting may occur for sparsely-sampled historical patterns. Future work will address multi-forcing conditioning, daily-resolved sequences, finer spatial scales, and bias correction with transfer learning from reanalysis data (Bouabid et al., 5 Oct 2025).

7. Significance Across Domains

The ESM-IF paradigm, spanning protein inverse folding, sensor data fusion, and earth system emulation, exemplifies model architectures that combine deep-learned structural priors, robust probabilistic inference, and task-specific optimization to address domain-specific performance objectives. The antibody design application, particularly SimBinder-IF, demonstrates that decoupling structure encoding from task optimization and leveraging efficient reference-free preference training yield parameter-efficient, highly performant models for function-aligned molecule generation (Zhao et al., 19 Dec 2025). In surveillance and environmental science, ESM-IF variants illustrate the value of modular fusion and distributional fidelity for downstream decision-making.

For further technical detail, implementation protocols, and empirical results, see the respective arXiv IDs: (Zhao et al., 19 Dec 2025, Taghavi et al., 2016, Bouabid et al., 5 Oct 2025).