The Role of Foundations and Technology Companies in Fuelling Optimism in Academic Research

The landscape of grant funding in North America has recently, rapidly, evolved, leading to a sense of nervousness and even pessimism within academia. However, remarkable technological advancements emerging from foundations and companies are counteracting this trend and fostering a renewed sense of optimism. 

In the last week, we have seen the unveiling of Evo-2, the largest AI model for biology, developed by the Arc Institute and the launch of Google’s AI co-scientist.

Evo-2, a collaborative effort between researchers at the Arc Institute, Stanford University, and NVIDIA, is accessible to scientists through web interfaces, with its software code, data, and parameters available for download. This model, trained on 128,000 genomes, spans the entire tree of life, from humans to single-celled organisms. 

The AI co-scientist, powered by Gemini 2.0, acts as a virtual research partner, aiding scientists in formulating hypotheses and research proposals, and accelerating the pace of scientific and biomedical discoveries. We have seen versions of this before, and are working on parallel themes for our clients at Digital Science, but it is encouraging to see that there is excitement for this at the very top of Google and Alphabet.

This wave of technological progress shows no signs of abating. The Astera Institute, a relatively new organization, aims to “accelerate science and technology for the benefit of humanity.” Their unique approach involves supporting entrepreneurially minded scientists in developing open science tools and technologies. Currently, they focus on creating predictive models of microbial behavior. Seemingly reverse engineering outcomes, by asking researchers to help share their academic data in a way that can move the needle like Alphafold.

At Digital Science, we share this optimism and are actively contributing to these advancements. Digital Science has a commitment to innovation, hand-in-hand with the responsible development of AI tools. With tools like the Dimensions AI Assistant, which provides users with contextualized synopses through extractive and abstractive summarization. Symplectic Elements now offers AI-powered abstracts and summaries, making research more discoverable and accessible while maintaining institutional control. We’re also hard at work on novel advances including:

• Auto-taxonomy: Employing AI/ML to automate classification, combining recent AI/ML techniques with traditional bibliometric methods to identify training data and train ML classifiers.
• AI Training: Preparing data for AI learning, such as processing complex scientific language from molecular biology.

• Horizon Scanning: Utilizing AI to identify emerging research trends. Our approach involves generating a 5,000 K-Means clustering model using publications from Dimensions.ai
• Literature Review: Leveraging generative AI to assist researchers in managing, organizing, and summarizing extensive scientific literature.
• Data Collection and Generation: Automating data collection from diverse sources and generating synthetic data to enhance existing datasets.

Applying AI to workflows gives researchers more time to focus on life-changing discoveries. We help researchers focus on higher value work, making them more productive with advanced AI tools.Our efforts are focused on addressing existing challenges in building upon previous research and AI-powered drug design. This includes creating high-quality, diverse, and interoperable datasets that are essential for training AI models. We’re doing this in many ways, through teams like metaphacts, Dimensions and Readcube.

Access to the right data and literature is crucial. At Digital Science, our approach has been to augment existing workflows with AI while ensuring end-to-end transparency – so the user can check and see what is happening at every stage. Readcube SLR is making significant strides in this area by:

• Automating literature collection, screening, and assessment using AI and intelligent workflows.
• Leveraging AI to refine searches and accelerate screening.
• Providing customisable templates.
• Offering AI-driven pre-filled suggestions with references.
• Implementing a QA process that combines human and AI validation.

In times of funding challenges, technology companies play a vital role in supporting and advancing academic research. These organisations can accelerate scientific discovery, improve data management, and foster a more optimistic and productive research environment. I don't know what the next 6 months of research funding looks like, but I couldn't be more optimistic for the future of academia and the fruits it yields.