Transformative Advances in Computational Protein Science and Beyond with Large Language Models

The field of computational protein science is undergoing a transformative shift with the integration of Large Language Models (LLMs), leading to the development of protein Language Models (pLMs) that significantly advance our understanding and capabilities in protein sequence-structure-function reasoning. These models are not only enhancing protein structure and function prediction but are also being applied in practical areas such as antibody and enzyme design, as well as drug discovery. The versatility and generalization capabilities of pLMs are setting new benchmarks in the field.

In parallel, LLMs are making significant inroads into various other domains, including automated scholarly paper review, healthcare, mental health, marketing management, research ethics review, graphic design, IT operations management, and materials discovery. These applications are characterized by a focus on enhancing efficiency, personalization, and accessibility, while also addressing the challenges of ethical considerations, data privacy, and the need for human oversight.

Noteworthy developments include the use of LLMs for high-throughput generation of metal-organic frameworks (MOFs) for carbon capture, the creation of application-specific LLMs to facilitate research ethics review, and the development of frameworks for evaluating the use of LLMs in health economics and outcomes research. Additionally, the integration of LLMs with small language models for adaptive log analysis and the empirical characterization of outages in public LLM services are contributing to the robustness and reliability of LLM applications.

Highlighted Papers:

  • Computational Protein Science in the Era of Large Language Models (LLMs): Introduces pLMs that grasp foundational protein knowledge, advancing sequence-structure-function reasoning.
  • MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow: Presents a high-throughput workflow for generating novel MOFs, showcasing significant CO$_2$ adsorption capacities.
  • Development of Application-Specific Large Language Models to Facilitate Research Ethics Review: Proposes IRB-specific LLMs to enhance the efficiency and quality of ethical review processes.
  • The ELEVATE-AI LLMs Framework: Offers a comprehensive framework for evaluating LLM-assisted research in health economics and outcomes research.
  • Empowering AIOps: Leveraging Large Language Models for IT Operations Management: Integrates LLMs with traditional predictive models to enhance IT operations management capabilities.

Sources

Computational Protein Science in the Era of Large Language Models (LLMs)

Large language models for automated scholarly paper review: A survey

How Large Language Models (LLMs) Extrapolate: From Guided Missiles to Guided Prompts

Participatory Assessment of Large Language Model Applications in an Academic Medical Center

Harnessing Large Language Models for Mental Health: Opportunities, Challenges, and Ethical Considerations

MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow

Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations

Development of Application-Specific Large Language Models to Facilitate Research Ethics Review

AI Based Font Pair Suggestion Modelling For Graphic Design

AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language Model

Large Language Models with Human-In-The-Loop Validation for Systematic Review Data Extraction

The ELEVATE-AI LLMs Framework: An Evaluation Framework for Use of Large Language Models in HEOR: an ISPOR Working Group Report

Empowering AIOps: Leveraging Large Language Models for IT Operations ManagementOperations Management

An Empirical Characterization of Outages and Incidents in Public Services for Large Language Models

Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review

Leveraging LLMs to Create a Haptic Devices' Recommendation System

Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents

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