# Bidirectional Learning for Offline Model-based Biological Sequence Design

arXiv: https://arxiv.org/abs/2301.02931 · HTML: https://mlsanity.com/paper/2301.02931/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2023.

## Built on by (most cited first, 20 of 20)

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- [Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences](https://mlsanity.com/paper/2306.03111/) (2023)
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- [Offline Model-Based Optimization: Comprehensive Review](https://mlsanity.com/paper/2503.17286/) (2025)
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- [Offline Model-Based Optimization by Learning to Rank](https://mlsanity.com/paper/2410.11502/) (2024)
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- [Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling](https://mlsanity.com/paper/2301.13154/) (2023)
- [Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking](https://mlsanity.com/paper/2502.17246/) (2025)
- [Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network](https://mlsanity.com/paper/2310.15342/) (2023)
- [On the Learnability of Offline Model-Based Optimization: A Ranking Perspective](https://mlsanity.com/paper/2603.04000/) (2026)
- [AffinityFlow: Guided Flows for Antibody Affinity Maturation](https://mlsanity.com/paper/2502.10365/) (2025)
- [Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach](https://mlsanity.com/paper/2505.06482/) (2025)
- [Generative Refinement for Low-Budget Black-Box Optimization](https://mlsanity.com/paper/2607.00691/) (2026)

## Abstract (arXiv)

Offline model-based optimization aims to maximize a black-box objective function with a static dataset of designs and their scores. In this paper, we focus on biological sequence design to maximize some sequence score. A recent approach employs bidirectional learning, combining a forward mapping for exploitation and a backward mapping for constraint, and it relies on the neural tangent kernel (NTK) of an infinitely wide network to build a proxy model. Though effective, the NTK cannot learn features because of its parametrization, and its use prevents the incorporation of powerful pre-trained Language Models (LMs) that can capture the rich biophysical information in millions of biological sequences. We adopt an alternative proxy model, adding a linear head to a pre-trained LM, and propose a linearization scheme. This yields a closed-form loss and also takes into account the biophysical information in the pre-trained LM. In addition, the forward mapping and the backward mapping play different roles and thus deserve different weights during sequence optimization. To achieve this, we train an auxiliary model and leverage its weak supervision signal via a bi-level optimization framework to effectively learn how to balance the two mappings. Further, by extending the framework, we develop the first learning rate adaptation module textitAdaptive-η, which is compatible with all gradient-based algorithms for offline model-based optimization. Experimental results on DNA/protein sequence design tasks verify the effectiveness of our algorithm. Our code is available~hrefhttps://anonymous.4open.science/r/BIB-ICLR2023-Submission/README.mdhere.