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
Background
The integration of artificial intelligence (AI) into healthcare is rapidly advancing, with profound implications for medical practice. However, a gap exists in formal AI education for pre-medical students. This study evaluates the effectiveness of the AI in Medicine Association (AIM), an extracurricular program designed to equip pre-medical students with foundational AI knowledge.
Methods
A quasi-experimental pretest-posttest control group design was employed, comparing knowledge acquisition between students participating in the AIM program (cohort group) and a control group of students not participating. The intervention spanned four weeks and included hands-on AI training, ethical considerations, data preprocessing, and model evaluation. Pretest and posttest assessments measured AI knowledge and pathology-related skills.
Results
Participants in the AIM program demonstrated significant improvements in both AI knowledge and pathology-related scores. The cohort group showed a large effect size across all measured domains, particularly in pathology, with Cohen’s d values ranging from 1.83 to 4.74. Statistical analysis confirmed robust, significant improvements in test scores (t-test and Mann-Whitney U test, p < 0.001). There was no significant correlation between previous AI experience or attitudes toward AI and overall score improvement.
Conclusions
The AIM program effectively improved pre-medical students’ understanding of AI and its application in medicine, particularly in pathology. This study highlights the potential of extracurricular programs to address the need for AI education in medical curricula, especially in the pre-medical phase, and suggests that such initiatives could serve as a model for other institutions seeking to integrate AI education into healthcare training.
TrIP2 is an advanced version of the transformer interatomic potential (TrIP) trained on the expanded ANI-2x data set, including more diverse molecular configurations with sulfur, fluorine, and chlorine. It leverages the equivariant SE(3)-transformer architecture, incorporating physical biases and continuous atomic representations. TrIP was introduced as a highly promising transferable interatomic potential, which we show here to generalize to new atom types with no alterations to the underlying model design. Benchmarking on COMP6 energy and force calculations, structure minimization tasks, torsion drives, and applications to molecules with unexpected conformational energy minima demonstrates TrIP2’s high accuracy and transferability. Direct architectural comparisons demonstrate superior performance against ANI-2x, while holistic model evaluations─including training data and level-of-theory considerations─show comparative performance with state-of-the-art models like AIMNet2 and MACE-OFF23. Notably, TrIP2 achieves state-of-the-art force prediction performance on the COMP6 benchmarks and closely approaches DFT-optimized structures in torsion drives and geometry optimization tasks. Without requiring any architectural modifications, TrIP2 successfully capitalizes on additional training data to deliver enhanced generalizability and precision, establishing itself as a robust and scalable framework capable of accommodating future expansions or applications to new domains with minimal reengineering.
Objective
To investigate the dose-response relationship between dietary sugar and T2D risk.
Methods
MEDLINE, Embase, CINAHL, Web of Science and Cochrane databases were searched through July 9, 2024 for prospective cohorts reporting relative measures of incident T2D risk by categories of dietary sugar (total, free, added, fructose, sucrose) or two beverage sources (non-diet SSB, fruit juice) in healthy adults (Prospero ID: CRD42023401800). Linear and restricted cubic spline dose-response models were fitted for each exposure and study-specific slopes and confidence intervals (CIs) were calculated. Heterogeneity was evaluated using Q-statistics. Risk of bias was evaluated using ROBINS-E tool GRADE approach was applied to assess the certainty of evidence.
Results
Of 10,384 studies, 29 cohorts were included: SSB:18 (n = 541,288); fruit juice:14 (n = 490,413); sucrose: 7 (n = 223,238); total sugar: 4 (n = 109,858); fructose: 5 (n = 158,136); and added sugar: 2 (n = 31,004). Studies were conducted in Europe (13), USA (11), Asia (6), Australia (4), and Latin America (3). Each additional serving of SSB and fruit juice was associated with a higher risk of T2D (RR: 1.25; 95% CI: 1.17–1.35 and RR: 1.05; 95% CI: >1.00–1.11, respectively; moderate certainty). In contrast, 20 g/d intakes of total sugar and sucrose were inversely associated with T2D (RR: 0.96; 95% CI: 0.94–0.98; low certainty; and RR: 0.95; 95% CI: 0.91– <1.00; moderate certainty, respectively). No associations for added sugar (RR: 0.99; 95% CI: 0.96–1.01; low certainty) or fructose (RR: 0.98; 95% CI: 0.83–1.15; very low certainty).
Conclusion
These findings suggest that dietary sugar consumed as a beverage (SSB and fruit juice) is associated with incident T2D risk. The results do not support the common assumption that dietary sugar (i.e., total sugar and sucrose), irrespective of type and amount, is consistently associated with increased T2D risk.