User Icon
Menu
Submit your Research
Journals
Cureus Journal of Engineering Cureus Journal of Computer Science Cureus Journal of Business and Economics Cureus Journal of Agriculture and Food Science Cureus Journal of AI-Augmented Research All Journals
Cureus for you
Manuscript Authors Peer Reviewers Universities and Institutes About Open Access Aims and Scope Author and Reviewer Guidelines FAQs About Cureus Journals Contact Cureus Journals
Channels
About Channels Browse Channels
Blogs
Sign In
Create an Account

How Researchers Are Augmenting Their Work with AI

DR

Dr. Rajmohan Pardeshi, Ph.D. | Jul 08, 2026

Linkedin | Google Scholar

The post-pandemic era has witnessed a dramatic surge in artificial intelligence (AI) research. Businesses across the globe are fiercely competing to get ahead in the AI race. The industry has invested billions of dollars in funding AI research. As a direct result, the AI ecosystem is now flooded with hundreds of tools that researchers can use to augment their work. Today’s blog post will feature some of the promising AI-based tools that researchers across disciplines are using to support their core research activities.   

AlphaFold for Biology Research 

Proteins play key roles in human physiology. However, determining the 3D structure of proteins is a labor-intensive and daunting task. Fortunately, Google DeepMind’s AlphaFold now provides a practical solution to this problem with the help of generative AI. According to Google DeepMind, over 2 million researchers across more than 190 countries are currently using AlphaFold’s protein structure database to facilitate various types of research activities revolving around the 3D structure of proteins. Quite interestingly, AlphaFold has predicted the 3D structures of over 200 million proteins so far. According to an article published in Nature, this versatile AI-based tool accurately predicts the 3D structures of proteins in a majority of cases. AlphaFold’s recent successes include accurately predicting the structure of PINK1—a protein involved in Parkinson’s disease, understanding the roles of faulty proteins implicated in cancer, and identifying the 3D structure of a physiologically relevant protein widely implicated in cardiac health. 

Over the years, Google DeepMind’s AlphaFold has evolved significantly. AlphaFold 3, for instance, is able to predict the joint structure of complexes involving proteins, nucleic acids, small molecules, ions, and modified residues with great accuracy.              

On a related note, did you know that Google DeepMind is offering fellowships to early-career researchers with a Ph.D. in machine learning, computer science, statistics, or any other relevant field? Click here to learn more about this training opportunity.  

IBM RoboRXN for Organic Chemists 

Detailed experimental procedures, often involving multistep syntheses of compounds, are routinely reported in approved patents and peer‑reviewed publications. However, the excessive detail required to reproduce these syntheses in the laboratory warrants human intervention. IBM’s RoboRXN automates this labor‑intensive extraction of information, thus freeing chemists to focus on higher‑order design and execution. A related article sheds light on how IBM RoboRXN can increase the overall efficiency of organic synthesis at scale using a versatile AI-based approach. According to IBM, integrating cloud technologies, AI, and automation has led to the successful deployment and adoption of IBM RoboRXN worldwide. An easy-to-understand real-world use case involves the recommendation of an optimal synthesis route for a new chemical structure drawn by a chemist in RoboRXN.        

Rosetta for Computational Biology 

Rosetta, a popular tool used by computational biologists worldwide, now integrates AI-driven methods in its software architecture. This versatile tool is used for modeling, designing, and analyzing proteins and other physiologically relevant biomolecules. Research teams working on therapeutic compounds commonly use Rosetta to study and model protein-drug interactions. Rosetta is also used to study the interactions of DNA and RNA with various proteins. This extremely useful AI-based tool also facilitates the engineering of antibodies and design of therapeutic vaccines.   

More AI Tools 

Every now and then, newer AI-based tools are being developed and deployed to support core research activities across disciplines. For instance, in the field of medicine, the platform Viz.ai provides over 50 FDA-approved AI algorithms for analyzing medical imaging data, thus streamlining biomedical research based on clinical image analysis. Computer science researchers, meanwhile, are routinely leveraging the power of an AI tool called CodeQL for identifying vulnerabilities across a codebase. In the field of simulation-based studies, Altair HyperWorks 2026 is doing a phenomenal job by empowering engineers to create fast and reliable systems that can drive real-world outcomes.  

By the end of 2026, researchers across disciplines may have many AI-based tools in their arsenal.  

Publishing with Cureus Journal of AI-Augmented Research  

Cureus Journal of AI-Augmented Research (CJAI), a fully open access Springer Nature journal, is dedicated to advancing science across disciplines by embedding AI at the heart of research. CJAI (EISSN: 3120‑4872) will publish rigorously validated research with a commitment to transparency, reproducibility, and human‑verified integrity, offering researchers a new venue for AI-facilitated innovation and discovery. Authors submitting manuscripts to CJAI disclose AI use via a structured two-tiered process, with editorial oversight and reviewer visibility throughout. 

CJAI Scope in Brief 

  • Novel AI applications

  • AI validation vis-à-vis traditional methods

  • Methodological and algorithmic AI advances

  • AI’s workflow and productivity impacts

  • Barriers to real-world AI adoption

  • AI vs. non-AI approaches

  • Reproducibility and AI study extensions

  • Rigorous AI studies reporting null or negative results 

To explore the full scope of CJAI, please click here.  

Information for Prospective CJAI Authors  

  • Core AI use: Manuscripts must integrate AI in core research activities such as experimental design, statistical analysis, or data collection.

  • Scale/complexity: AI may also be used to enable work at scales and complexities beyond manual methods (e.g., analysis of large datasets).

  • Not eligible: Manuscripts limited to traditional algorithms, or those applying AI solely for drafting, editing, or formatting, will not be considered for peer review.

  • LLM usage: Manuscripts combining core AI use with language refinement tools (e.g., LLMs) may be considered, provided all AI usage is fully documented.

  • Responsibility: Authors remain fully responsible for the accuracy, integrity, originality, and interpretation of all content. 

FAQs

Why should I publish with Cureus Journals?  

Authors who publish with Cureus Journals derive the following benefits:  

  • Open access platform

  • Quality peer review

  • Benefits from trusted partners (Google Scholar, Crossref, and Dimensions)

  • Inclusive publishing

  • Submit via a user-friendly interface

  • Track research community engagement with ease (claps, comments, views, downloads, and SIQ™)  

What are the benefits of publishing in an open access journal?  

Manuscript authors can derive the benefits mentioned below by publishing in an open access journal. Click here to learn more.  

  • Global reach and increased citations

  • Wider collaboration

  • Higher Altmetric scores

  • More article downloads

  • More news and policy mentions

  • More citations by patents

  • Easy licensing terms

  • More scope for interdisciplinary, multidisciplinary, and convergence research

  • Compliance with funding agency mandates

  • Better funding opportunities  

Would you like to submit your manuscript to Cureus Journal of AI-Augmented Research sometime soon? Make sure you review the journal scope and author guidelines.