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.
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Selected Publications
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.
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.
Accurate predictions of interatomic energies and forces are essential for high quality molecular dynamic simulations (MD). Machine learning algorithms can be used to overcome limitations of classical MD by predicting ab initio quality energies and forces. SE(3)-equivariant neural network allow reasoning over spatial relationships and exploiting the rotational and translational symmetries. One such algorithm is the SE(3)-Transformer, which we adapt for the ANI-1x dataset. Our early experimental results indicate through ablation studies that deeper networks—with additional SE(3)-Transformer layers—could reach necessary accuracies to allow effective integration with MD. However, faster implementations of the SE(3)-Transformer will be required, such as the recently published accelerated version by Milesi.