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The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson’s disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KD lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants adopted strikingly diverging screening strategies. Machine learning-accelerated methods achieved similar results to brute force (e.g., exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

Advances in artificial intelligence (AI) in the medical sector necessitate the development of AI literacy among future physicians. This article explores the pioneering efforts of the AI in Medicine Association (AIM) at Brigham Young University, which offers a framework for undergraduate pre-medical students to gain hands-on experience, receive principled education, explore ethical considerations, and learn appraisal of AI models. By supplementing formal, university-organized pre-medical education with a student-led, faculty-supported introduction to AI through an extracurricular academic association, AIM alleviates apprehensions regarding AI in medicine early and empowers students preparing for medical school to navigate the evolving landscape of AI in healthcare responsibly.

Background

The quality of carbohydrate intake, as measured by the glycemic index (GI), has not been evaluated nationally over the past two decades in the United States (US).

Objective

We aimed to develop a comprehensive and nationally representative dietary GI and glycemic load (GL) database from 1999-2018 National Health and Nutrition Examination Survey (NHANES) and to examine GI and GL time trends and sub-population differences.

Design

We employed an artificial intelligence (AI)-enabled model to match GI values from two GI databases to food codes from US Department of Agriculture, which were manually validated. We examined nationally representative distributions of dietary GI and GL from 1999-2018 using the multistage, clustered sampling design of NHANES.

Results

Assigned GI values covered 99.9% of total carbohydrate intake. The initial AI accuracy was 75.0%, with 31.3% retained after manual curation guided by substantive domain expertise. A total of 7,976 unique food codes were matched to GI values, of which soft drinks and white bread were top contributors to dietary GI and GL. Of the 49,205 NHANES adult participants, the mean dietary GI was 55.7 [95% CI: 55.5, 55.8] and energy-adjusted dietary GL was 133.0 [132.3, 133.8]. From 1999 to 2018, dietary GI and GL decreased by 4.6% and 13.8%, respectively. Dietary GL was higher among females 134.6 [133.8, 135.5] than males 131.3 [130.3, 132.3], those with ≤ high school degree 137.7 [136.8, 138.7] compared to those with ≥ college degree 126.5 [125.3, 127.7], and those living under the poverty level 140.9 [139.6, 142.1] compared to above. Differences in race were observed (Black adults 139.4 [138.2, 140.7]; White adults 131.6 [130.5, 132.6]).

Conclusion

We developed a national GI and GL database to facilitate large-scale and high-quality surveillance or cohort studies of diet and health outcomes in the US.