Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Artificial Intelligence (AI), particularly in the domains of machine learning and generative models, has fundamentally altered the landscape of scientific research. This paper explores the integration of AI across various scientific disciplines, highlighting its role in data analysis, hypothesis generation, and experimental design. By examining recent breakthroughs such as protein folding prediction and advanced climate modeling, we demonstrate how AI accelerates the pace of discovery. Furthermore, this article addresses the ethical considerations, challenges in reproducibility, and the "black box" nature of deep learning systems. Ultimately, we argue that while AI is a powerful catalyst for innovation, it necessitates robust frameworks for transparency and human-AI collaboration to ensure the integrity of scientific progress.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
