Zero-Shot Stability Prediction

Updated 30 June 2025

Zero-shot stability prediction is a method that forecasts the impact of molecular, structural, or data distribution changes using models trained on diverse, general datasets.
It leverages foundation models and conditional adaptation layers to extrapolate reliable predictions across fields like protein engineering, natural language processing, and neural architecture search.
Empirical evaluations demonstrate its practical utility by reducing prediction variance and enhancing accuracy in applications such as high-throughput mutation screening and continual learning.

Zero-shot stability prediction denotes the task of forecasting the stability consequences of molecular, structural, or data distribution changes using a model trained on general, rather than instance-specific, data—requiring no new labeled examples for the target configuration. This paradigm is foundational in domains such as protein and molecular engineering, language modeling, neural architecture search, continual learning, and applied prediction tasks across modalities. The principal utility lies in the ability to extrapolate reliable predictions to novel conditions, sequences, or classes in a principled and scalable manner.

1. Foundational Principles and Methodologies

Zero-shot stability prediction is formalized in a variety of ways depending on domain:

Protein Science: Models receive a protein sequence, its structure (often predicted), and a proposed mutation or set of mutations. The models estimate the effect of these sequence changes on stability (commonly in terms of ΔΔG, the change in free energy upon mutation) or functional fitness, without being trained on ground-truth stability data for the specific protein or variant (Frellsen et al., 5 Jun 2025, Sharma et al., 23 Apr 2025, Tan et al., 2023).
NLP: Pretrained LLMs are assessed for their prediction “stability” in zero-shot classification under changes to input prompts, output label phrasing, or task generalization (Qian et al., 4 Apr 2025, Zhou et al., 2022).
Architecture Search and Continual Learning: Predictors estimate the stability or prospective performance of neural network architectures or successive task learning without supervised retraining (Le et al., 2023, Lu et al., 2023).

A common structure for zero-shot stability prediction models comprises:

Foundation models pre-trained on broad, diverse data (e.g., protein LLMs, transformer architectures for gene or event sequences, LLMs for text).
Conditional adaptation layers (such as adapters or side-conditioning schemes) that integrate information from target perturbations or changes (e.g., drug embeddings, protein mutation).
Zero-shot inference protocols that operate on data unseen during training, leveraging domain representations or semantic embeddings for transferability.

Representative mathematical formulation in protein stability prediction uses the log-likelihood ratio between wild-type and mutant sequences, conditioned on 3D structure, as a predictive surrogate for thermodynamic free energy differences:

$-\ln \frac{p_\theta(\vec{a}' | \vec{x})}{p_\theta(\vec{a} | \vec{x})}$

where $\vec{a}$ and $\vec{a}'$ are wild-type and mutant sequences, and $\vec{x}$ is the structure (Frellsen et al., 5 Jun 2025).

In NLP, prompt-based zero-shot models estimate class probabilities via next-token prediction or, more robustly, aggregated multi-token probabilities (e.g., Placeholding Parallel Prediction; P3) (Qian et al., 4 Apr 2025).

2. Empirical Performance and Benchmarks

The empirical efficacy of zero-shot stability prediction frameworks is well established across domains:

Protein Stability: Inverse folding models (e.g., ESM-IF1) leveraging log-likelihood ratios show high correlation with measured protein stability changes. Incorporating ensemble averaging and unfolded state corrections enhances accuracy (average correlation increases observed on Protein G, Guerois, and VAMP-seq datasets) (Frellsen et al., 5 Jun 2025).
Structure-Based Fitness Prediction: Multi-modal models integrating sequence, evolutionary (MSA), and structure-based features (e.g., TranceptEVE L, ProtSSN, simple sum ensembles) consistently improve zero-shot prediction, with multi-modal ensembles setting strong performance baselines on the ProteinGym benchmark (Sharma et al., 23 Apr 2025).
NLP Prompt Stability: Placeholding Parallel Prediction (P3) yields up to 98% reduction in standard deviation of zero-shot classification accuracy across prompt variations, with robust performance even in absence of a prompt (Qian et al., 4 Apr 2025). Swarm distillation regularization (prompt consistency) increases both stability (Fleiss’ kappa) and average classification accuracy across multiple tasks (Zhou et al., 2022).
Continual Learning: The IBCL framework generates zero-shot models for any user-specified stability-plasticity trade-off instantly via convex combination of parameter distributions, achieving up to 23% higher average per-task accuracy and robust resistance to forgetting compared to retrain-based baselines (Lu et al., 2023).
Neural Architecture Search: Fourier sum of sines encoded neural predictors support stable, accurate zero-shot evaluation and ranking of architectures across heterogeneous search spaces, outperforming both handcrafted and graph-convolutional predictors (Le et al., 2023).

3. Factors Affecting Zero-Shot Stability and Limitations

Performance and stability of zero-shot predictions are influenced by several factors:

Partitioning Variability: In zero-shot classification and transfer regimes (e.g., zero-shot class splits), performance can vary significantly depending on the classes chosen for training and testing. Reporting only average accuracy is misleading; standard deviation across splits and statistical significance testing are necessary (Molina et al., 2021).
Prompt Sensitivity: In prompt-based NLP, minor perturbations in input prompts lead to substantial performance fluctuations, termed “prompt brittleness.” Standard next-token models are highly sensitive; P3 and consistency regularization methods mitigate this by leveraging multi-position outputs or regularizing across prompt variants (Qian et al., 4 Apr 2025, Zhou et al., 2022).
Data Domain Mismatch: In protein fitness, using predicted structures from AlphaFold 2 as model input is effective for ordered regions but problematic in disordered regions, where the absence of reliable structure reduces predictive power—structure-based predictors are less effective, and masking is sometimes employed (Sharma et al., 23 Apr 2025).
Ensemble Approaches: Ensembling submodels trained on different subsets of classes or points may provide slight stability gains but typically only marginally reduce prediction variance (Molina et al., 2021).

4. Practical Applications

Zero-shot stability prediction supports a range of real-world tasks:

Protein Engineering: Enables high-throughput screening of mutation candidates for improved stability or function without requiring new experiments, facilitating rational enzyme and therapeutic design (Frellsen et al., 5 Jun 2025, Sharma et al., 23 Apr 2025, Tan et al., 2023).
Genetic Variant Interpretation: Supports instant assessment of variant pathogenicity, especially for rare or novel mutations for which experimental data are absent (Sharma et al., 23 Apr 2025).
Molecular Perturbation in Drug Discovery: Predicts transcriptional response to new drugs or in new cell lines using single-cell foundation models with minimal fine-tuning via drug-conditional adapters (Maleki et al., 18 Dec 2024).
Clinical and Health Forecasting: Models such as ETHOS enable simulation of patient-specific treatment trajectories and risk stratification without task-specific retraining, supporting real-time decision making (Renc et al., 30 Jul 2024).
Resource-Efficient Continual Learning: IBCL allows on-demand generation of solutions for arbitrary stability–plasticity preferences in adaptive systems, avoiding the need to retrain for each new trade-off (Lu et al., 2023).

Several methodological recommendations emerge:

Robust Evaluation: Always report not only mean accuracy but also standard deviation (across class or language splits), and use statistical tests (e.g., Wilcoxon signed-rank test) to assess methodological significance (Molina et al., 2021).
Modeling Disordered Regions: In protein modeling, account for the reduced reliability of structure-based predictions in intrinsically disordered regions by masking, fallback to sequence-only modeling, or including disorder-aware metrics (Sharma et al., 23 Apr 2025).
Predictor Ensembles: Simple multi-modal ensembles, constructed without additional joint training, frequently enhance zero-shot stability prediction and establish competitive performance ceilings (Sharma et al., 23 Apr 2025).
Regularization & Prompt Aggregation: In LLM-based tasks, aggregate outputs over multiple prompts or output positions (e.g., P3) or explicitly regularize for prediction consistency to minimize prompt-induced variability (Qian et al., 4 Apr 2025, Zhou et al., 2022).
Feature Selection & Multi-task Learning: In multilingual transfer or cross-task generalization, robust feature selection (e.g., via block sparsity/group lasso) and multi-task regression are crucial for stable, interpretable prediction (Ahuja et al., 2022).

6. Theoretical Insights and Future Directions

Recent work elucidates the theoretical connection between model-internal statistics and physical principles:

Free Energy Foundations: Inverse folding model likelihoods can be directly interpreted in terms of thermodynamic free energy differences, with ensemble averaging and explicit unfolded-state correction yielding predictions more closely aligned with physical theory (Frellsen et al., 5 Jun 2025).
Unfolded State Modeling: Incorporating background amino acid frequencies from intrinsic disorder as proxies for the unfolded state can improve prediction of stability changes, as can using generative models to better sample representative ensembles (Frellsen et al., 5 Jun 2025).
Prompt-invariant and Task-agnostic Modeling: Emerging methods suggest soft placeholder tokens and foundation model extensions that natively incorporate prompt-invariant scoring can substantially advance stability and generalization (Qian et al., 4 Apr 2025).

Continued research is anticipated in refining evaluation protocols, hybridizing structure- and sequence-based predictions, extending to complex stability metrics (e.g., binding, assembly), and broadening zero-shot stability prediction to new modeling modalities and application areas.

Summary Table: Representative Zero-Shot Stability Prediction Approaches

Application	Model/Technique	Key Principle / Limitation
Protein Stability	Inverse folding (ESM-IF1) (Frellsen et al., 5 Jun 2025, Sharma et al., 23 Apr 2025)	Log-likelihood ratio of mutant/wild w/ structure; improvement with ensemble avg., disorder-proxy unfolded state
NLP Zero-shot	P3, Swarm distillation (Qian et al., 4 Apr 2025, Zhou et al., 2022)	Multi-token aggregation / prompt consistency regularization reduces prompt sensitivity
Continual Learning	IBCL (Lu et al., 2023)	Constant-time convex hull of posteriors enables Pareto-optimal trade-off model generation
Neural Arch. Search	Fourier-encoded neural pred. (Le et al., 2023)	Transferable, invariant encoding generalizes across search spaces
Drug Response	Single-cell FM w/ adapter (Maleki et al., 18 Dec 2024)	Efficient conditional adapters enable zero-shot molecular perturbation prediction

Zero-shot stability prediction is positioned as a key methodology for robust, efficient deployment of predictive models in diverse challenging settings, contingent on rigorous evaluation, physically and semantically grounded modeling, and continual methodological refinement.