From Data to Discovery: How Foundation Models Are Accelerating Scientific Innovation with Generative AI

2025 · doi:10.22541/au.175641355.59755488/v1
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Foundation models and generative AI are accelerating scientific discovery across disciplines by enabling hypothesis generation, simulation, and pattern identification, though challenges in bias, interpretability, and equitable access remain.

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The paper studies how generative artificial intelligence and large foundation models are changing the pace, scope, and methods of scientific discovery, describing approaches such as hypothesis generation, data augmentation, and predictive modeling across multiple domains including biomedicine. Using a high-level conceptual overview, it argues that foundation models trained on large multimodal datasets can generalize and reason contextually to uncover patterns and accelerate knowledge creation, citing examples like protein folding predictions and novel compound discovery. A major caveat explicitly noted is that foundational models face challenges related to bias, interpretability, reproducibility, unequal access to compute resources, and ethical dual-use risks, so credibility depends on responsible governance. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

The rise of generative artificial intelligence (AI) and large-scale foundation models is redefining the pace, scope, and methodology of scientific discovery. Traditionally, computational science has relied on simulations, statistical modeling, and narrowly focused algorithms, which, while effective, often lacked scalability and adaptability across domains. Foundation models trained on vast, multimodal datasets introduce a new paradigm, enabling machines not only to process information but to generate hypotheses, simulate phenomena, and uncover hidden patterns in ways previously unimaginable. By leveraging their capacity for generalization and contextual reasoning, these models accelerate knowledge creation across biomedicine, materials science, climate research, physics, and the social sciences. In practice, generative AI is transforming research through hypothesis generation, data augmentation, and predictive modeling, while reducing costs and democratizing access to advanced scientific tools. Breakthroughs such as protein folding predictions, novel compound discovery, and AI-driven climate simulations demonstrate the transformative impact of these technologies. However, significant challenges persist. Issues of bias, interpretability, reproducibility, and unequal access to computational resources raise concerns about the reliability and fairness of AI-driven science. Ethical risks, including dual-use applications in biotechnology and the potential misuse of generated knowledge, further underscore the need for careful oversight. As research institutions and policymakers begin to establish governance frameworks, the emphasis on transparency, accountability, and open science becomes critical to ensuring that AI-driven discovery remains credible and inclusive. Looking ahead, the integration of generative AI with symbolic reasoning, the development of domainspecialized foundation models, and the emergence of self-driving laboratories suggest a future in which human-AI collaboration fundamentally redefines how knowledge is created. This paper argues that while foundation models present extraordinary opportunities to accelerate discovery, their responsible and equitable deployment will determine whether they fulfill their promise as engines of scientific innovation.
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Abstract

The rise of generative artificial intelligence (AI) and large-scale foundation models is redefining the pace, scope, and methodology of scientific discovery. Traditionally, computational science has relied on simulations, statistical modeling, and narrowly focused algorithms, which, while effective, often lacked scalability and adaptability across domains. Foundation models trained on vast, multimodal datasets introduce a new paradigm, enabling machines not only to process information but to generate hypotheses, simulate phenomena, and uncover hidden patterns in ways previously unimaginable. By leveraging their capacity for generalization and contextual reasoning, these models accelerate knowledge creation across biomedicine, materials science, climate research, physics, and the social sciences. In practice, generative AI is transforming research through hypothesis generation, data augmentation, and predictive modeling, while reducing costs and democratizing access to advanced scientific tools. Breakthroughs such as protein folding predictions, novel compound discovery, and AI-driven climate simulations demonstrate the transformative impact of these technologies. However, significant challenges persist. Issues of bias, interpretability, reproducibility, and unequal access to computational resources raise concerns about the reliability and fairness of AI-driven science. Ethical risks, including dual-use applications in biotechnology and the potential misuse of generated knowledge, further underscore the need for careful oversight. As research institutions and policymakers begin to establish governance frameworks, the emphasis on transparency, accountability, and open science becomes critical to ensuring that AI-driven discovery remains credible and inclusive. Looking ahead, the integration of generative AI with symbolic reasoning, the development of domainspecialized foundation models, and the emergence of self-driving laboratories suggest a future in which human-AI collaboration fundamentally redefines how knowledge is created. This paper argues that while foundation models present extraordinary opportunities to accelerate discovery, their responsible and equitable deployment will determine whether they fulfill their promise as engines of scientific innovation. Supplementary Material File (from data to discovery.pdf) - Download - 146.17 KB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

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Authors Metrics & Citations Metrics Article Usage 370views 142downloads Citations Download citation Elevane Dave, Folorunsho Adeola, Dave Noel. From Data to Discovery: How Foundation Models Are Accelerating Scientific Innovation with Generative AI. Authorea. 28 August 2025. DOI: https://doi.org/10.22541/au.175641355.59755488/v1 DOI: https://doi.org/10.22541/au.175641355.59755488/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.

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