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Integration of AI tools to enhance undergraduate biochemistry research

Date
August 20, 2025

Computational folding of proteins using AI-based tools such as AlphaFold and ESMFold has generated over 600 million protein structures. The next critical step is to predict functions for these proteins and apply this knowledge to real-world problems, such as enzyme engineering or disease research. This wealth of protein structure information provides an almost endless number of research projects suitable for undergraduate students. In addition to wet-lab techniques, students can leverage AI-driven tools to enhance their research. This presentation will discuss a range of AI-based and AI-enhanced tools appropriate for use in undergraduate protein biochemistry research. This includes applications such as protein function prediction, active site identification, molecular docking experiments, and protein mutagenesis. Examples of student workflows and projects will be provided, highlighting how AI can be integrated into students' studies. Challenges and limitations of using these AI-tools with undergraduates will also be shared.

Presenter

Speaker Image for Bonnie Hall
Associate Professor, Grand View University

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