APL machine learning最新文献

{"title":"Automatic forward model parameterization with Bayesian inference of conformational populations.","authors":"Robert M Raddi, Tim Marshall, Vincent A Voelz","doi":"10.1063/5.0287423","DOIUrl":"10.1063/5.0287423","url":null,"abstract":"<p><p>To quantify how well theoretical predictions of structural ensembles agree with experimental measurements, we depend on the accuracy of forward models (FMs). These models are computational frameworks that generate observable quantities from molecular configurations based on empirical relationships linking specific molecular properties to experimental measurements. Bayesian Inference of Conformational Populations (BICePs) is a reweighting algorithm that reconciles simulated ensembles with ensemble-averaged experimental observations, even when such observations are sparse and/or noisy. This is achieved by sampling the posterior distribution of conformational populations under experimental restraints as well as sampling the posterior distribution of uncertainties due to random and systematic error. In this study, we enhance the algorithm for the refinement of empirical FM parameters. We introduce and evaluate two novel methods for optimizing FM parameters. The first method treats FM parameters as nuisance parameters, integrating over them in the full posterior distribution. The second method employs variational minimization of a quantity called the BICePs score that reports the free energy of \"turning on\" the experimental restraints. This technique, coupled with improved likelihood functions for handling experimental outliers, facilitates force field validation and optimization, as illustrated in recent studies [R. M. Raddi <i>et al.</i>, J. Chem. Theory Comput. <b>21</b>, 5880-5889 (2025) and R. M. Raddi and V. A. Voelz, \"Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian inference of conformational populations,\" arXiv:2402.11169 (2024)]. Using this approach, we refine parameters that modulate the Karplus relation, crucial for accurate predictions of <i>J</i>-coupling constants based on dihedral angles (<i>ϕ</i>) between interacting nuclei. We validate this approach first with a toy model system and then for human ubiquitin, predicting six sets of Karplus parameters for <math> <mmultiscripts><mrow><mi>J</mi></mrow> <mrow> <msup><mrow><mi>H</mi></mrow> <mrow><mi>N</mi></mrow> </msup> <msup><mrow><mi>H</mi></mrow> <mrow><mi>α</mi></mrow> </msup> </mrow> <none></none> <mprescripts></mprescripts> <none></none> <mrow><mn>3</mn></mrow> </mmultiscripts> </math> , <math> <mmultiscripts><mrow><mi>J</mi></mrow> <mrow> <msup><mrow><mi>H</mi></mrow> <mrow><mi>α</mi></mrow> </msup> <msup><mrow><mi>C</mi></mrow> <mrow><mo>'</mo></mrow> </msup> </mrow> <none></none> <mprescripts></mprescripts> <none></none> <mrow><mn>3</mn></mrow> </mmultiscripts> </math> , <math> <mmultiscripts><mrow><mi>J</mi></mrow> <mrow> <msup><mrow><mi>H</mi></mrow> <mrow><mi>N</mi></mrow> </msup> <msup><mrow><mi>C</mi></mrow> <mrow><mi>β</mi></mrow> </msup> </mrow> <none></none> <mprescripts></mprescripts> <none></none> <mrow><mn>3</mn></mrow> </mmultiscripts> </math> , <math> <mmultiscripts><mrow><mi>J</mi></mrow> <mrow> <msup><mrow","PeriodicalId":520238,"journal":{"name":"APL machine learning","volume":"4 1","pages":"016102"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12818351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AutoSAS: A new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation.","authors":"Duncan R Sutherland, Rachel Ford, Yun Liu, Tyler B Martin, Peter A Beaucage","doi":"10.1063/5.0271073","DOIUrl":"10.1063/5.0271073","url":null,"abstract":"<p><p>The advancement of artificial-intelligence driven autonomous experiments demands physics-based modeling and decision-making processes, not only to improve the accuracy of the experimental trajectory but also to increase trust by allowing transparent human-machine collaboration. High-quality structural characterization techniques (e.g., x ray, neutron, or static light scattering) are a particularly relevant example of this need: they provide invaluable information but are challenging to analyze without expert oversight. Here, we introduce AutoSAS, a novel framework for human-aside-the-loop automated data classification. AutoSAS leverages human-defined candidate models, high-throughput combinatorial fitting, and information-theoretic model selection to generate both classification results and quantitative structural descriptors. We implement AutoSAS in an open-source package designed for use with the Autonomous Formulation Laboratory for x-ray and neutron scattering-based optimization of multi-component liquid formulations. In a first application, we leveraged a set of expert defined candidate models to classify, refine the structure, and track transformations in a model injectable drug carrier system. We evaluated four model selection methods and benchmarked them against an optimized machine learning classifier, and the best approach was one that balanced quality of the fit and complexity of the model. AutoSAS not only corroborated the critical micelle concentration boundary identified in previous experiments but also discovered a second structural transition boundary not identified by the previous methods. These results demonstrate the potential of AutoSAS to enhance autonomous experimental workflows by providing robust, interpretable model selection, paving the way for more reliable and insightful structural characterization in complex formulations.</p>","PeriodicalId":520238,"journal":{"name":"APL machine learning","volume":"3 3","pages":"036111"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12376025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dreaming of electrical waves: Generative modeling of cardiac excitation waves using diffusion models.","authors":"Tanish Baranwal, Jan Lebert, Jan Christoph","doi":"10.1063/5.0194391","DOIUrl":"10.1063/5.0194391","url":null,"abstract":"<p><p>Electrical waves in the heart form rotating spiral or scroll waves during life-threatening arrhythmias, such as atrial or ventricular fibrillation. The wave dynamics are typically modeled using coupled partial differential equations, which describe reaction-diffusion dynamics in excitable media. More recently, data-driven generative modeling has emerged as an alternative to generate spatio-temporal patterns in physical and biological systems. Here, we explore denoising diffusion probabilistic models for the generative modeling of electrical wave patterns in cardiac tissue. We trained diffusion models with simulated electrical wave patterns to be able to generate such wave patterns in unconditional and conditional generation tasks. For instance, we explored the diffusion-based (i) parameter-specific generation, (ii) evolution, and (iii) inpainting of spiral wave dynamics, including reconstructing three-dimensional scroll wave dynamics from superficial two-dimensional measurements. Furthermore, we generated arbitrarily shaped bi-ventricular geometries and simultaneously initiated scroll wave patterns inside these geometries using diffusion. We characterized and compared the diffusion-generated solutions to solutions obtained with corresponding biophysical models and found that diffusion models learn to replicate spiral and scroll wave dynamics so well that they could be used for data-driven modeling of excitation waves in cardiac tissue. For instance, an ensemble of diffusion-generated spiral wave dynamics exhibits similar self-termination statistics as the corresponding ensemble simulated with a biophysical model. However, we also found that diffusion models produce artifacts if training data are lacking, e.g., during self-termination, and \"hallucinate\" wave patterns when insufficiently constrained.</p>","PeriodicalId":520238,"journal":{"name":"APL machine learning","volume":"2 3","pages":"036113"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142374000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}