Diffusion on language model embeddings for protein sequence generation
(2403.03726)Abstract
Protein design requires a deep understanding of the inherent complexities of the protein universe. While many efforts lean towards conditional generation or focus on specific families of proteins, the foundational task of unconditional generation remains underexplored and undervalued. Here, we explore this pivotal domain, introducing DiMA, a model that leverages continuous diffusion on embeddings derived from the protein language model, ESM-2, to generate amino acid sequences. DiMA surpasses leading solutions, including autoregressive transformer-based and discrete diffusion models, and we quantitatively illustrate the impact of the design choices that lead to its superior performance. We extensively evaluate the quality, diversity, distribution similarity, and biological relevance of the generated sequences using multiple metrics across various modalities. Our approach consistently produces novel, diverse protein sequences that accurately reflect the inherent structural and functional diversity of the protein space. This work advances the field of protein design and sets the stage for conditional models by providing a robust framework for scalable and high-quality protein sequence generation.
Overview
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ICML 2024 emphasizes electronic submissions in PDF format, combining manuscript, appendices, and references into a single file to enhance review efficiency.
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Strict adherence to formatting details such as 10 point Times font, Type-1 fonts for text integrity, and specific placement for figures, tables, and references is mandatory.
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The conference enforces ethical standards by requiring author anonymity during initial submissions to support a double-blind review process and rejects simultaneous submissions.
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The guidelines highlight the importance of empirical evaluation and ablation studies, ensuring submissions advance the field through rigorous analysis and novel contributions.
A Comprehensive Overview of Submission and Formatting Instructions for ICML 2024
Introduction
The annual International Conference on Machine Learning (ICML) stands as a pivotal gathering for scholars, researchers, and professionals within the machine learning domain to exchange insights, progress, and forecasts about the discipline's trajectory. The 2024 submission guidelines serve as a beacon of structure for prospective contributors, delineating the requisite formatting, submission procedures, and ethical considerations indispensable for deliberation in the conference proceedings. This blog post aims to simplify and elucidate these cardinal regulations, ensuring authors navigate the submission landscape with efficacy and compliance.
Electronic Submission and Preparations
Submissions for ICML 2024 pivot entirely on an electronic interface, dismissing any form of email or hard copy submissions. In a novel twist, appendices must be amalgamated with the main manuscript and references into a singular file for submission, adhering strictly to a PDF format. This consolidation aims at steering clear of oversight during the review process. Paramount details include:
- PDF Format Exclusivity: The manuscript, inclusive of appendices, must abide strictly to a PDF format.
- Page Limitations: An 8-page limit is enforced on the main body of the paper, with appendices and references permitted additional space. Authors of accepted papers will have the leverage to expand the main body by an extra page in their final submission.
- Author Anonymity: Initial submissions must obscure author identities, a decree supporting the double-blind review ethos of ICML.
Style and Formatting Nuances
The document’s stylistic and typographic elements bear significant weight. The adherence to a 10 point Times font throughout the textual content is mandatory, punctuated by exacting specifications regarding figure captions, table placements, and the encapsulation of references. Critical notations entail:
- Font Integrity: The mandatory use of Type-1 fonts to avert complications in the transcoding of the document.
- Element Positioning: Specific directives on the placement and formatting of figures, tables, and references to maintain consistency and readability.
- Reference Formatting: A chronological ordering in citations, with a comprehensive detailing including page numbers where feasible, ensuring a uniform presentation of the bibliography.
Ethical Compliance and Anonymity
ICML’s staunch commitment to ethical scholarliness is evident through its stringent policies on simultaneous submissions, which are summarily rejected if found to be under consideration elsewhere. The anonymity clause extends to censoring any form of author identification within the submission text, fostering an unbiased review process. Additionally, any form of prior work by the authors should be cited in a manner that preserves the review's blind nature.
Evaluation Matrices and Ablation Studies
The guidelines underscore a distinctive emphasis on rigorous empirical evaluation, with a performance comparison table delineated in the paper serving as a template. The inclusion of an ablation study serves not only to benchmark the proposed DiMA model against prevailing architectures but also highlights the incremental enhancements afforded by various model iterations, evidencing a methodical approach to model refinement.
Implications and Theoretical Contributions
While maintaining a detached narrative, it’s paramount to underscore the paper’s theoretical and practical implications within the machine learning community. The quantitative leaps in performance metrics postulated by the DiMA model accentuate its potential for improving predictive accuracies in protein sequence modeling. Moreover, the theoretical underpinnings detailed in the model's architecture propose a novel paradigm that may spur further research within the domain.
Conclusion and Future Directions
In sum, the ICML 2024 submission and formatting instructions provide a detailed blueprint for authors to follow, ensuring their work is presented in a coherent and standardized manner. The guidelines are designed to facilitate a fair and rigorous review process, encouraging the submission of high-quality papers that advance the field of machine learning. Through adherence to these guidelines, authors can contribute to the rich tapestry of knowledge that ICML represents, pushing the boundaries of what is possible in machine learning research.