High School Student Jumpstarts Research Journey With Mentorship From Stevens Researcher

Collaboration between university faculty and students passionate about innovation is a hallmark of a top research institution. So how can a high school student eager to contribute to scientific research make an impact before they’ve even enrolled in college?

For Ibrahim Wichka, a student at Bergen County Academies with a passion for computational drug discovery, the answer came through mentorship from Pin-Kuang Lai, an assistant professor in the Department of Chemical Engineering and Materials Science at Stevens Institute of Technology. Their collaboration led to a peer-reviewed study published in Computational and Structural Biotechnology.

The study explores the potential of artificial intelligence (AI) in accelerating drug discovery for celiac disease, a chronic autoimmune disorder affecting about 1% of the global population, according to Lai, whose research focuses on machine learning, molecular simulations and high-throughput screening for drug design.

"The only current treatment is a strict gluten-free diet, which often proves unsustainable," said Lai. "But celiac disease already has a studied target protein, Transglutaminase 2 (TG2), which makes it suitable for small molecule drug discovery."

Accelerating drug discovery through computational design

In this study, Wichka and Lai focused on computational drug design, an approach that overcomes inefficiencies associated with traditional drug discovery methods. Traditional drug discovery involves testing potential drugs in small in vitro bioassays, experiments that evaluate the effects of substances on living cells or biomolecules. The process helps identify molecules suitable for optimization and clinical trials but it often requires individual testing of thousands of molecules.

"The process can become so tedious and expensive that companies must drop studies on a certain target, eliminating the potential for an effective treatment," said Lai.

By contrast, computational drug design streamlines multiple phases of the discovery process. "Computational drug design uses statistical methods and, more recently, AI-based approaches to develop tools capable of rapidly screening thousands of molecules in a short period," Lai said, explaining that developing models with high accuracy reduces the need for extensive laboratory testing, which can save significant time and money while achieving reliable results. "These tools identify the most promising candidates worth further study within minutes," he added.

In their project, Wichka and Lai applied machine learning to rapidly screen and optimize potential drug candidates, resulting in the creation of an innovative tool called "Celiac Informatics." This tool evaluates TG2 inhibitors, analyzes bioactivity and generates drug-likeness reports, enabling pharmaceutical researchers to efficiently identify promising candidates.

"Machine learning allows us to improve model accuracy and streamline the classification of potential drug candidates," said Lai.

For Wichka, this project demonstrated the vital role AI can play in revolutionizing drug discovery. "AI will be the most important factor in accelerating medicine," he said. "Publishing studies and innovative web tools in journals is the best method of making this acceleration possible and spreading awareness of new technologies that can make the process more efficient."

This approach not only accelerates the drug discovery process but also creates new opportunities for addressing therapeutic targets that might otherwise have been abandoned.

Broadening the research community at the pre-college level

The collaboration between Wichka and Lai started after Dr. Deok-Yang Kim, head of Bergen County Academies’ chemistry and nanotechnology research program, attended a presentation by Lai. Kim recognized the alignment between Lai’s expertise and Wichka’s interest in computational drug discovery, creating an opportunity for Wichka to engage in advanced research.

"I was able to expand my knowledge of data science and AI applications in medicine," Wichka said. "Many students are familiar with AI’s role in diagnostics, but this research showed me how technology, chemistry and medicine can integrate to advance drug discovery."

Weekly meetings between Lai and Wichka structured the research process. This approach allowed Wichka to analyze data, interpret results and brainstorm ideas with Lai. Through the sessions, Wichka deepened his understanding of scientific methodology and the iterative nature of research.

Wichka and Lai plan to leverage advancements in generative AI to design novel molecules for celiac disease treatment, with further testing and optimization on the horizon. 

Lai noted that mentoring high school students like Wichka offers mutual benefits. "Wichka’s project, as a new research direction, creates opportunities for additional collaborations within Stevens, benefiting both the lab and the broader research community," he said.

For Wichka, whose parents work in software engineering and medicine, the experience solidified his ambition to work at the intersection of these fields.

"I grew up with one parent in the software engineering field and the other in medicine, so I guess a career at the intersection of the two fields might be engraved into my blood," Wichka said. "I’d like to continue working as a researcher in this interdisciplinary field during college and hopefully find my way into the AI sector of a large pharmaceutical or big tech company—or possibly even create an AI-based biotech startup of my own."

Lai plans to provide additional mentorship to Wichka. He is also working with another Bergen County Academies student helping advance research opportunities and innovation at the pre-college level.