Concept Bottleneck Language Models For protein design (2411.06090v2)

Abstract: We introduce Concept Bottleneck Protein LLMs (CB-pLM), a generative masked LLM with a layer where each neuron corresponds to an interpretable concept. Our architecture offers three key benefits: i) Control: We can intervene on concept values to precisely control the properties of generated proteins, achieving a 3 times larger change in desired concept values compared to baselines. ii) Interpretability: A linear mapping between concept values and predicted tokens allows transparent analysis of the model's decision-making process. iii) Debugging: This transparency facilitates easy debugging of trained models. Our models achieve pre-training perplexity and downstream task performance comparable to traditional masked protein LLMs, demonstrating that interpretability does not compromise performance. While adaptable to any LLM, we focus on masked protein LLMs due to their importance in drug discovery and the ability to validate our model's capabilities through real-world experiments and expert knowledge. We scale our CB-pLM from 24 million to 3 billion parameters, making them the largest Concept Bottleneck Models trained and the first capable of generative LLMing.

• The paper introduces a CB-pLM that integrates an interpretable concept layer to enhance control and debuggability in protein design.
• It employs a linear decoder within a masked protein language model to achieve a threefold boost in controlling biological properties across over 80 tasks.
• The model scales from 24 million to 3 billion parameters while preserving natural sequence generation, aiding advancements in drug discovery and synthetic biology.

Analysis of the Concept Bottleneck LLMs for Protein Design

The paper "Concept Bottleneck LLMs for Protein Design" presents an innovative approach to the interpretability, controllability, and debuggability challenges faced by protein LLMs (pLMs) using Concept Bottleneck Models (CBMs). These models insert an interpretable layer into neural networks, wherein each neuron corresponds to a human-understandable concept, facilitating nuanced control over model predictions. This approach addresses the opacity of traditional transformer-based pLMs, which hinders their applicability in critical fields like healthcare and drug discovery.

The proposed Concept Bottleneck Protein LLMs (CB-pLM), a specific application of CBMs to protein LLMing, offer significant advantages: they allow direct manipulation of biological concepts to generate proteins with desired properties, provide interpretability through a transparent decision-making process, and enable straightforward debugging. These models are tested against various conditional pLM architectures and demonstrate superior control, achieving a threefold improvement in modulating concept values over baseline models, with a notable performance on over 80 single and multi-property control tasks.

Architectural Innovations

The CB-pLM extends the traditional masked pLM by integrating a "concept bottleneck" layer, facilitating explicit handling of human-understandable concepts. It employs a linear decoder that maintains transparency in token prediction, enabling the interpretation of individual concept contributions. This aligns with the goal of intuitive model understanding, critical for tasks involving high-stakes applications. Notably, CB-pLM scales from smaller models with 24 million parameters to larger configurations with up to 3 billion parameters, demonstrating scalability without compromising interpretability or performance compared to unconstrained models.

Significant Numerical Results and Comparative Analysis

In rigorous evaluations, CB-pLM demonstrates substantial improvements in control accuracy over conditional models, particularly in tasks necessitating fine-grained control, such as reducing the Grand Average of Hydropathy (GRAVY) in a monoclonal antibody. In head-to-head comparisons with state-of-the-art protein design models like PropEn and LaMBO-2, CB-pLM not only achieves comparable modifications in protein properties but also retains the naturalness of the sequence—an essential factor in the viability of in silico designed proteins.

Implications for Protein Design and Future Directions

The integration of CBMs into pLMs marks a pivotal advancement in bioinformatics and protein design, where interpretability, methodological transparency, and control are paramount. By aligning model predictions with conceptual understanding, CB-pLM fosters greater trust and reliability in model outputs among domain experts, facilitating its adoption in drug discovery and synthetic biology.

Looking forward, CB-pLM opens avenues for the application of CBMs across various domains beyond protein design, promoting research into more generalized LLMs that integrate human-understandable concepts. The potential to incorporate additional concepts through fine-tuning, and to generate novel concept combinations, enhances CB-pLM's appeal for continuous learning and adaptation in dynamic research environments.

Conclusion

The Concept Bottleneck Protein LLMs represent a significant enhancement in the field of protein design, underscoring the practical and theoretical advantages of embedding human-understandable concepts within neural network architectures. As this methodology matures, it sets the stage for more robust, interpretable, and controllable AI systems tailored for specialized scientific domains.