Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences (2411.04165v1)

Abstract: LLMs for biological and chemical sequences enable crucial applications such as drug discovery, protein engineering, and precision medicine. Currently, these LLMs are predominantly based on Transformer architectures. While Transformers have yielded impressive results, their quadratic runtime dependency on the sequence length complicates their use for long genomic sequences and in-context learning on proteins and chemical sequences. Recently, the recurrent xLSTM architecture has been shown to perform favorably compared to Transformers and modern state-space model (SSM) architectures in the natural language domain. Similar to SSMs, xLSTMs have a linear runtime dependency on the sequence length and allow for constant-memory decoding at inference time, which makes them prime candidates for modeling long-range dependencies in biological and chemical sequences. In this work, we tailor xLSTM towards these domains and propose a suite of architectural variants called Bio-xLSTM. Extensive experiments in three large domains, genomics, proteins, and chemistry, were performed to assess xLSTM's ability to model biological and chemical sequences. The results show that models based on Bio-xLSTM a) can serve as proficient generative models for DNA, protein, and chemical sequences, b) learn rich representations for those modalities, and c) can perform in-context learning for proteins and small molecules.

• The paper presents advanced domain-specific xLSTM models that enable efficient generative and predictive modeling of complex biological and chemical sequences.
• It offers linear runtime advantages over Transformer models, effectively handling long sequences typical in genomics and proteomics.
• Bio-xLSTM demonstrates superior performance in DNA, protein, and molecule generation tasks, suggesting promising implications for AI-driven drug discovery.

Bio-xLSTM: A Novel Approach to Modeling Biological and Chemical Sequences

The paper "Bio-xLSTM: Generative Modeling, Representation, and In-Context Learning of Biological and Chemical Sequences" presents an advanced exploration of the capabilities of xLSTM, an emerging recurrent neural network architecture, specifically tailored towards biological and chemical sequence modeling. By systematically extending xLSTM into a suite of variants termed Bio-xLSTM, the researchers introduce tailored architectures for DNA (DNA-xLSTM), proteins (Prot-xLSTM), and chemical sequences (Chem-xLSTM), each designed to address domain-specific challenges.

Enhanced Sequence Modeling

In contrast to traditional Transformer-based models that are predominantly utilized for long sequence data in biological domains, Bio-xLSTM offers significant computational advantages. Transformers, despite their efficacy, exhibit a quadratic runtime dependency on sequence length, which creates challenges for long biological sequences like those encountered in genomics. xLSTM, by maintaining linear runtime dependency and constant-memory decoding, provides a compelling alternative for these extensive applications.

The results presented reveal that Bio-xLSTM excels in learning rich representations and generative modeling, performing competitively against state-of-the-art methods across multiple domains:

1. DNA Sequences: DNA-xLSTM showcases superior performance in both causal and masked LLMing tasks, particularly excelling in long-sequence settings where it challenges the limitations of current Transformer-based and state-space models (SSMs) like Caduceus.
2. Protein Sequences: Prot-xLSTM significantly improves upon previous models by capturing the complexities of protein sequences, enabling effective homology-aware protein LLMing. The model demonstrates remarkable results in protein fitness prediction and homology-conditioned sequence generation tasks.
3. Chemical Sequences: Chem-xLSTM attains impressive outcomes in unconditional molecule generation, yielding the lowest Fréchet ChemNet Distance (FCD) scores, and excels at conditional molecule generation without fine-tuning, showcasing its in-context learning capabilities.

Implications and Challenges

The implications of this research are both practical and theoretical. Practically, the introduction of Bio-xLSTM could usher in a new era of foundation models tailored for molecular biology, potentially transforming drug discovery pipelines by allowing for more efficient modeling of complex biochemical systems. On a theoretical level, the work highlights the potential benefits of extending recurrent neural network architectures in ways that incorporate domain-specific inductive biases, paving the way for further exploration into architectures that balance efficiency and expressivity.

However, several challenges and limitations must be considered. Foremost, the current models depend heavily on extensive hyperparameter optimization and character-level tokenization, which may not fully capture the hierarchical nature of biological sequences or the underlying structural properties in the case of proteins. Furthermore, biases present in training datasets might influence model predictions, highlighting the need for comprehensive evaluation across diverse datasets and biological contexts.

Future Directions

Looking forward, expanding the Bio-xLSTM suite to include additional molecular features, exploring hierarchical modeling approaches (such as motif-level representations in genomics), and assessing performance in larger parameter regimes could provide richer insights and broader applicability. Additionally, enhancing the integration of structural data in proteins or pursuing novel tokenization strategies for complex molecules could further boost performance and accuracy.

The exploration of Bio-xLSTM evidences a compelling shift towards specialized sequential models that address domain-specific challenges in biological data, holding promise for substantial advancements in AI-driven life sciences research.