Regeneron Young Scientist Awards

Regeneron and Society for Science presents $50,000 to two First Place projects. These finalists are selected for their commitment to innovation in tackling challenging scientific questions, using authentic research practices and creating solutions to the problems of tomorrow.

Regeneron (NASDAQ: REGN) is a leading biotechnology company that invents life-transforming medicines for people with serious diseases. Founded and led for over 30 years by physician-scientists, our unique ability to repeatedly and consistently translate science into medicine has led to nine FDA-approved treatments and numerous product candidates in development, almost all of which were homegrown in our laboratories. Our medicines and pipeline are designed to help patients with eye diseases, allergic and inflammatory diseases, cancer, cardiovascular and metabolic diseases, pain, infectious diseases and rare diseases.

Regeneron is accelerating and improving the traditional drug development process through our proprietary VelociSuite technologies, such as VelocImmune, which uses unique genetically humanized mice to produce optimized fully human antibodies and bispecific antibodies, and through ambitious research initiatives such as the Regeneron Genetics Center, which is conducting one of the largest genetics sequencing efforts in the world.

For additional information about the company, please visit www.regeneron.com or follow @Regeneron on Twitter.

2023

Saathvik Kannan, David H. Hickman High School, MO, United States of America

Saathvik received $50,000 for using biocomputational methods to understand the causes of heightened infectivity in the disease mpox after it reemerged in 2022. Saathvik’s approach, named Bioplex, uses a combination of machine learning and three-dimensional comparative protein modeling to decode structures like those that enable the mpox virus to replicate. This allowed him to identify the mutations in the virus that likely made it more infectious as well as other mutations that could make it resistant to antibiotics. Saathvik believes scientists will also be able to apply Bioplex to future outbreaks of other viruses.

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Project Abstract: CBIO068 – BIO-PLEX: An Innovative Biocomputational Approach

2023

Teepakorn Keawumdee, Pannathorn Siri, and, Poon Trakultangmun, Bangkok Christian College, Thailand

This team received $50,000 for developing an innovative incubation chamber that promotes the survival of the green lacewing insect, a natural predator of the mealybug, a harmful pest. In nature, the green lacewing has a low survival rate, but the team’s new system increased the lacewing’s survival rate five-fold. In field tests, their incubation system was an effective alternative to insecticides and lowered the mealybug population density nearly four-fold.

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Project Abstract: ANIM024T – Innovation for Optimizing Lacewing Survivability

2022

Abdullah Al-Ghamdi, Al-Hussan High School, Dammam, Eastern, Saudi Arabia

Abdullah received $50,000 for modifying a metal-organic material so it could be used to both extract hydrogen from water and safely store it for clean energy production. Because the materials he added are relatively inexpensive, his work could significantly reduce the cost of hydrogen extraction and storage.

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Project Abstract: EGSD018 — Engineering a Bifunctional Metal: Organic Framework for Efficient and Cost-Effective Hydrogen Productions and Storage

2022

Rishab Jain, Westview High School, Portland, OR, United States of America

Rishab received $50,000 for developing an AI-based model to enable rapid and cost-effective production of drugs, such as recombinant COVID-19 vaccines, using synthetic DNA engineering. His model is trained to optimize the selection of genetic codes in DNA.

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Project Abstract: ENBM074 — Synthetic DNA Engineering With ICOR: Improving Codon Optimization With Recurrent Neural Networks Towards Efficient, Low-Cost, High-Efficacy Recombinant Vaccine and Pharmaceutical Manufacturing

2021

Catherine Kim, Jericho High School, NY, United States of America

Catherine received $50,000 for creating a novel hierarchical machine learning model that is able to predict adverse drug reactions with 91% accuracy, along with their underlying biological mechanisms.

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Project Abstract: CBIO080 — Novel Prediction of Adverse Drug Reactions and Underlying Pathological Mechanisms via Hierarchical Classification

2021

Daniel Shen, William G. Enloe High School, NC, United States of America

Daniel received $50,000 for his development of an AI-powered facial-cue control module. Its high speed accelerates scientific discoveries, medicine research, and many other fields driven by large amounts of temporal data. The new algorithm has been adopted by researchers in Sepsis Shock diagnosis and Hurricane predictions.

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Project Abstract: SOFT012 — Development of an AI-Powered Facial-cue Control Module