SE(3)-Stochastic Flow Matching for Protein Backbone Generation

The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions -- i.e. the group $\text{SE}(3)$ -- enabling accurate modeling of protein backbones. We first introduce FoldFlow-Base, a simulation-free approach to learning deterministic continuous-time dynamics and matching invariant target distributions on $\text{SE}(3)$. We next accelerate training by incorporating Riemannian optimal transport to create FoldFlow-OT, leading to the construction of both more simple and stable flows. Finally, we design FoldFlow-SFM, coupling both Riemannian OT and simulation-free training to learn stochastic continuous-time dynamics over $\text{SE}(3)$. Our family of FoldFlow, generative models offers several key advantages over previous approaches to the generative modeling of proteins: they are more stable and faster to train than diffusion-based approaches, and our models enjoy the ability to map any invariant source distribution to any invariant target distribution over $\text{SE}(3)$. Empirically, we validate FoldFlow, on protein backbone generation of up to $300$ amino acids leading to high-quality designable, diverse, and novel samples.Comment: ICLR 2024 Spotligh

Iterated denoising energy matching for sampling from Boltzmann densities

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient -- and no data samples -- to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant $n$-body particle systems. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5\times$ faster, which allows it to be the first method to train using energy on the challenging $55$-particle Lennard-Jones system

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient -- and no data samples -- to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant $n$-body particle systems. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5\times$ faster, which allows it to be the first method to train using energy on the challenging $55$-particle Lennard-Jones system.Comment: Published at ICML 2024. Code for iDEM is available at https://github.com/jarridrb/de

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201

Effect of the COVID-19 pandemic on emergency department utilization of computed tomography scans of appendicitis and diverticulitis

Abstract Purpose Investigating the effect of the COVID-19 lockdown on adult patient visits, computed tomography (CT) abdominal scans, and presentations of appendicitis and diverticulitis, to emergency departments (ED) in St. John’s NL. Methods A retrospective quantitative analysis was applied, using ED visits and Canadian Triage and Acuity Scale (CTAS) scores. mPower (Nuance Communications, UK) identified CT abdominal scan reports, which were categorized into (1) normal/other, (2) appendicitis, or (3) diverticulitis. Time intervals included pre-lockdown (January–February), lockdown (March–June), and post-lockdown (July–August). Data from 2018 to 2019 (January–August) were used to generate expected patient volumes for 2020, and pre- and post-lockdown were included to control for other variables outside the lockdown. Results Chi-squared goodness of fit tested for deviations from predicted means for 2018–2019. Compared to expectations, daily ED visits from January to August 2020 showed a significant (p &lt; 0.001) decrease in patient volumes independent of gender, age, and CTAS scores. During and post-lockdown, CT abdominal scans did not drop in proportion to patient volume. Appendicitis presentations remained indifferent to lockdown, while diverticulitis presentations appeared to wane, with no difference in combined complicated cases in comparison to what was expected. Conclusion During lockdown, significantly fewer patients presented to the ED. The proportion of ordered CT abdominal scans increased significantly per person seen, without change in CTAS scores. Considering combined pathology cases increased during the lockdown, ED physicians were warranted in increasing abdominal imaging as patients did not avoid the ED. This may have resulted from a change in clinical practice where the uncertainty of COVID-19 increased CT scan usage. </jats:sec

School-Based Nutrition Programs for Adolescents in Dodoma, Tanzania: A Situation Analysis

Background: Tanzania has a double burden of malnutrition, including a high prevalence of undernutrition and an increasing prevalence of overweight and obesity among adolescents. Schools present a valuable opportunity to reach a large section of the country’s adolescent population with nutrition-oriented interventions. Objective: The objective of this study was to assess the current state of adolescent school nutrition interventions in Dodoma, Tanzania, with emphasis on 3 potential school-based nutrition interventions, school vegetable gardens, school meals, and education (on nutrition, agriculture, and water, sanitation, and hygiene). Methods: Focus group discussions were conducted with several regional and district-level governmental stakeholders, including health, education, and agricultural officers. Ten public secondary schools were visited, and interviews with school administrators, teachers, students, and parents were conducted. Results: All stakeholders interviewed supported interventions to improve school-based nutrition, including school gardens, school feeding, and nutrition education. All 10 schools visited had some experience providing school meals, but parents’ contributions were essential for the program’s sustainability. Most schools visited had land available for a school garden program, but water availability could be challenging during certain times of the year. The teachers interviewed expressed that the curriculum on nutrition education was highly theoretical and did not allow students to practice the knowledge and skills they learned in the classroom. Conclusions: The current school-based approach to tackling the double burden of adolescent malnutrition in Dodoma is localized and ad hoc. To leverage the potential of schools as a platform for nutrition interventions, integrated and policy-mandated interventions are needed. </jats:sec

Iterated denoising energy matching for sampling from Boltzmann densities

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient—and no data samples—to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant -body particle systems. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains 2−5× faster, which allows it to be the first method to train using energy on the challenging 55-particle Lennard-Jones system