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The Consortium of Molecular Design at BYU provides cutting edge interdisciplinary research opportunities for students to push the envelope for protein engineering and drug discovery.

We use close collaboration between laboratories at BYU in Physics, Chemistry, Computer Science, LifeSciences, and Engineering to tackle these challenging topics from all angles.

We actively seek industrial collaboration and support for our efforts and are excited to explore mutually beneficial application of all state-of-the-art technologies to revolutionize molecular design.


News and Events

Selected Publications

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Dennis Della Corte, Connor J. Morris, Wendy M. Billings, Jacob Stern, Austin J. Jarrett, Bryce Hedelius, and Adam Bennion

Effective mentoring of undergraduate students is a growing requirement for the promotion of faculty at many universities. It is often challenging for young investigators to define a successful mentoring strategy, partially due to the absence of a broadly accepted definition of what mentoring should entail. To overcome this, an outcome-oriented mentoring framework was developed and used with more than 25 students over three years. It was found that a systematic mentoring approach can help students quickly realize their scientific potential and result in meaningful contributions to science. This report especially shows how the Critical Assessment of Protein Structure Prediction (CASP14) challenge was used to amplify student research efforts. As a result of this challenge, multiple publications, presentations and scholarships were awarded to the participating students. The mentoring framework continues to see much success in allowing undergraduate students, including students from underrepresented groups, to foster scientific talent and make meaningful contributions to the scientific community.

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Talmage Porter, Michael M. Vaka, Parker Steenblik, and Dennis Della Corte

Molten salts are important thermal conductors used in molten salt reactors and solar applications. To use molten salts safely, accurate knowledge of their thermophysical properties is necessary. However, it is experimentally challenging to measure these properties and a comprehensive evaluation of the full chemical space is unfeasible. Computational methods provide an alternative route to access these properties. Here, we summarize the developments in methods over the last 70 years and cluster them into three relevant eras. We review the main advances and limitations of each era and conclude with an optimistic perspective for the next decade, which will likely be dominated by emerging machine learning techniques. This article is aimed to help researchers in peripheral scientific domains understand the current challenges of molten salt simulation and identify opportunities to contribute.

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Standardizing data is crucial for preserving and exchanging scientific information. In particular, recording the context in which data were created ensures that information remains findable, accessible, interoperable, and reusable. Here, we introduce the concept of self-reporting data assets (SRDAs), which preserve data and contextual information. SRDAs are an abstract concept, which requires a suitable data format for implementation. Four promising data formats or languages are popularly used to represent data in pharma: JCAMP-DX, JSON, AnIML, and, more recently, the Allotrope Data Format (ADF). Here, we evaluate these four options in common use cases within the pharmaceutical industry using multiple criteria. The evaluation shows that ADF is the most suitable format for the implementation of SRDAs.

Research Opportunities

Dennis Della Corte
Dennis Della Corte (Materials Physics )
  • ProSPr - Protein Structure Prediction

    A cross divisional team of physicists, computer scientists, biologists and chemists implements a novel protein structure prediction pipeline to solve one of the oldest challenges in computational biophysics: The Protein Folding Problem.

    We will apply our pipeline to a global community wide blind test in 2020 called CASP14. 

    The work entails:

    - training of convolutional neural networks

    - design of simulation algorithms

    - high performance super computer usage

    - chemical and biological evaluation of results

  • Radical SAM Engineering

    Together with the Chemistry department at BYU, we are developing  algorithms that aid the systematic design of novel enzymes.

    These enzymes can be applied to a variety of use cases, such as fertilizer production, detergent production, or drug production.