Continuing Education for Structural Biology Mentors

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PROJECT SUMMARY/ABSTRACT The structural biology and molecular modeling market is expected to reach USD 18.1 Billion with a Compound Annual Growth Rate (CAGR) of 17.1% during the forecast period of 2022-2028 which has the potential to drive substantial demand for a workforce of the future. While the significance of advancement in cutting-edge Cryo- EM data processing to improve and accelerate drug discovery to treat diseases is driving innovation and economic growth and happening in the pharma industry, many structural biology investigators and would-be investigators are caught without the enhanced research training to use rapidly evolving and sophisticated computational tools, including AI and deep learning tools, proficiently to help the pharma industry accelerate drug discovery for society. Underrepresented minority (URM) representation in cutting-edge biomedical research fields is not expected to be higher than what was reported in a 2019 NSF survey, which showed URM as a percentage of the academic doctoral workforce at 8.9%, or a 2021 AAMC report which found diversity for URM at academic leadership ranks in American academic medical colleges at about 5.8%. In particular, the high costs associated with Cryo-EM research present a significant barrier for smaller institutions like Minority Serving Institutions (MSIs) and Historically Black Colleges and Universities (HBCUs) to train their students for the structural biology-based biomedical career of the future. To address these challenges, the Structural Biology Grid (SBGrid) Consortium at Harvard Medical School and Meharry Medical College, an HBCU, will extend their existing partnership to improve diversity and deliver an enhanced research training program across the entire community for structural biology investigators on the current Cryo-EM data processing workflow that includes AI components, necessary skills, knowledge, and best practices to help them stay on top and be productive in their careers in the biomedical research workforce. Additionally, the program is designed not just to impart training for the principal investigator participants but also to increase sustainability and scalability by enabling them with an evidence-based, integrated gear of data science training model and system for structural biology comprising a complete set of training program material with training datasets, a collection of structural biology data analysis and visualization software, and a sustainable training and computing environment with supporting resources that they could use in their home institutions to provide continuing education and longitudinal mentoring to their laboratory members and students who are pursuing next-generation biomedical careers in structural biology.