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Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals. 利用机器学习分析静息态 fMRI BOLD 信号识别阿片类药物使用障碍的大脑功能网络
Pub Date : 2024-11-26
Ahmed Temtam, Megan A Witherow, Liangsuo Ma, M Shibly Sadique, F Gerard Moeller, Khan M Iftekharuddin

Understanding the neurobiology of opioid use disorder (OUD) using resting-state functional magnetic resonance imaging (rs-fMRI) may help inform treatment strategies to improve patient outcomes. Recent literature suggests temporal characteristics of rs-fMRI blood oxygenation level-dependent (BOLD) signals may offer complementary information to functional connectivity analysis. However, existing studies of OUD analyze BOLD signals using measures computed across all time points. This study, for the first time in the literature, employs data-driven machine learning (ML) modeling of rs-fMRI BOLD features representing multiple time points to identify region(s) of interest that differentiate OUD subjects from healthy controls (HC). Following the triple network model, we obtain rs-fMRI BOLD features from the default mode network (DMN), salience network (SN), and executive control network (ECN) for 31 OUD and 45 HC subjects. Then, we use the Boruta ML algorithm to identify statistically significant BOLD features that differentiate OUD from HC, identifying the DMN as the most salient functional network for OUD. Furthermore, we conduct brain activity mapping, showing heightened neural activity within the DMN for OUD. We perform 5-fold cross-validation classification (OUD vs. HC) experiments to study the discriminative power of functional network features with and without fusing demographic features. The DMN shows the most discriminative power, achieving mean AUC and F1 scores of 80.91% and 73.97%, respectively, when fusing BOLD and demographic features. Follow-up Boruta analysis using BOLD features extracted from the medial prefrontal cortex, posterior cingulate cortex, and left and right temporoparietal junctions reveals significant features for all four functional hubs within the DMN.

利用静息态功能磁共振成像(rs-fMRI)了解阿片类药物使用障碍(OUD)的神经生物学,有助于为改善患者预后的治疗策略提供信息。最近的文献表明,rs-fMRI 血液氧合水平依赖性(BOLD)信号的时间特征可为功能连通性分析提供补充信息。然而,现有的 OUD 研究使用计算所有时间点的测量值来分析 BOLD 信号。本研究在文献中首次采用了数据驱动的机器学习(ML)模型,对代表多个时间点的 rs-fMRI BOLD 特征进行建模,以确定将 OUD 受试者与健康对照组(HC)区分开来的感兴趣区域。根据三重网络模型,我们从默认模式网络(DMN)、显著性网络(SN)和执行控制网络(ECN)获得了 31 名 OUD 受试者和 45 名 HC 受试者的 rs-fMRI BOLD 特征。然后,我们使用 Boruta ML 算法识别出具有统计学意义的 BOLD 特征,将 OUD 与 HC 区分开来,确定 DMN 是 OUD 最突出的功能网络。此外,我们还进行了脑活动图谱分析,结果显示 OUD 在 DMN 中的神经活动增强。我们进行了 5 倍交叉验证分类(OUD vs. HC)实验,研究功能网络特征在融合和不融合人口特征的情况下的鉴别力。在融合 BOLD 和人口统计学特征时,DMN 显示出最强的分辨能力,平均 AUC 和 F1 分数分别达到 80.91% 和 73.97%。使用从内侧前额叶皮层、后扣带回皮层以及左右颞顶叶交界处提取的 BOLD 特征进行的后续 Boruta 分析显示,DMN 中的所有四个功能枢纽都具有重要特征。
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引用次数: 0
Multisacle Jones Polynomial and Persistent Jones Polynomial for Knot Data Analysis. 结数据分析的多尺度Jones多项式和持久Jones多项式。
Pub Date : 2024-11-26
Ruzhi Song, Fengling Li, Jie Wu, Fengchun Lei, Guo-Wei Wei

Many structures in science, engineering, and art can be viewed as curves in 3-space. The entanglement of these curves plays a crucial role in determining the functionality and physical properties of materials. Many concepts in knot theory provide theoretical tools to explore the complexity and entanglement of curves in 3-space. However, classical knot theory primarily focuses on global topological properties and lacks the consideration of local structural information, which is critical in practical applications. In this work, two localized models based on the Jones polynomial, namely the multiscale Jones polynomial and the persistent Jones polynomial, are proposed. The stability of these models, especially the insensitivity of the multiscale and persistent Jones polynomial models to small perturbations in curve collections, is analyzed, thus ensuring their robustness for real-world applications.

科学、工程和艺术中的许多结构都可以看作是三维空间中的曲线。这些曲线的缠结在决定材料的功能和物理性质方面起着至关重要的作用。结理论中的许多概念为探索三维空间中曲线的复杂性和纠缠性提供了理论工具。然而,经典的结理论主要关注全局拓扑性质,缺乏对实际应用中至关重要的局部结构信息的考虑。本文提出了基于Jones多项式的两种局部化模型,即多尺度Jones多项式和持久Jones多项式。分析了这些模型的稳定性,特别是多尺度和持久的Jones多项式模型对曲线集合中的小扰动的不敏感性,从而保证了它们在实际应用中的鲁棒性。
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引用次数: 0
Quantifying information stored in synaptic connections rather than in firing patterns of neural networks. 量化存储在突触连接中的信息,而不是神经网络的放电模式。
Pub Date : 2024-11-26
Xinhao Fan, Shreesh P Mysore

A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of connections among the constituent neurons. However, in contrast to the well-established theory for quantifying information encoded by the firing patterns of neural networks, little is known about quantifying information encoded by its synaptic connections. Here, we develop a theoretical framework using continuous Hopfield networks as an exemplar for associative neural networks, and data that follow mixtures of broadly applicable multivariate log-normal distributions. Specifically, we analytically derive the Shannon mutual information between the data and singletons, pairs, triplets, quadruplets, and arbitrary n-tuples of synaptic connections within the network. Our framework corroborates well-established insights about storage capacity of, and distributed coding by, neural firing patterns. Strikingly, it discovers synergistic interactions among synapses, revealing that the information encoded jointly by all the synapses exceeds the 'sum of its parts'. Taken together, this study introduces an interpretable framework for quantitatively understanding information storage in neural networks, one that illustrates the duality of synaptic connectivity and neural population activity in learning and memory.

我们理解生物神经网络和人工神经网络的一个基石是,它们通过组成神经元之间的连接强度来存储信息。然而,与由神经网络发射模式编码的信息量化理论相比,对其突触连接编码的信息量化知之甚少。在这里,我们开发了一个理论框架,使用连续Hopfield网络作为联想神经网络的范例,并遵循广泛适用的多元对数正态分布的混合数据。具体来说,我们分析推导了数据与网络内突触连接的单态、对态、三态、四态以及任意n元组之间的香农互信息。我们的框架证实了关于神经放电模式的存储容量和分布式编码的成熟见解。引人注目的是,它发现了突触之间的协同作用,揭示了所有突触共同编码的信息超过了“部分之和”。综上所述,本研究为定量理解神经网络中的信息存储引入了一个可解释的框架,该框架说明了突触连接和神经群体活动在学习和记忆中的对偶性。
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引用次数: 0
Immobility of isolated swarmer cells due to local liquid depletion. 局部液体耗竭导致离体群集细胞不动。
Pub Date : 2024-11-26
Ajesh Jose, Benjamín Pérez-Estay, Shira Omer Bendori, Avigdor Eldar, Daniel B Kearns, Gil Ariel, Avraham Be'er

Bacterial swarming is a complex phenomenon in which thousands of self-propelled rod-shaped cells move coherently on surfaces, providing an excellent example of active matter. However, bacterial swarming is different from most studied examples of active systems because single isolated cells do not move, while clusters do. The biophysical aspects underlying this behavior are unclear. In this work we explore the case of low local cell densities, where single cells become temporarily immobile. We show that immobility is related to local depletion of liquid. In addition, it is also associated with the state of the flagella. Specifically, the flagellar bundles at (temporarily) liquid depleted regions are completely spread-out. Our results suggest that dry models of self-propelled agents, which only consider steric alignments and neglect hydrodynamic effects, are oversimplified and are not sufficient to describe swarming bacteria.

细菌群是一种复杂的现象,成千上万的自我推进的杆状细胞在表面上连贯地移动,提供了一个很好的活性物质的例子。然而,细菌群与大多数研究过的活性系统的例子不同,因为单个分离的细胞不移动,而集群可以移动。这种行为背后的生物物理因素尚不清楚。在这项工作中,我们探讨了低局部细胞密度的情况,其中单个细胞暂时无法移动。我们表明,不动性与局部液体耗竭有关。此外,它还与鞭毛的状态有关。具体来说,鞭毛束在(暂时)液体耗尽的区域完全展开。我们的研究结果表明,仅考虑位向排列而忽略流体动力效应的自推进介质的干燥模型过于简化,不足以描述群集细菌。
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引用次数: 0
Bayesian Variable Selection for High-Dimensional Mediation Analysis: Application to Metabolomics Data in Epidemiological Studies.
Pub Date : 2024-11-26
Youngho Bae, Chanmin Kim, Fenglei Wang, Qi Sun, Kyu Ha Lee

In epidemiological research, causal models incorporating potential mediators along a pathway are crucial for understanding how exposures influence health outcomes. This work is motivated by integrated epidemiological and blood biomarker studies, investigating the relationship between long-term adherence to a Mediterranean diet and cardiometabolic health, with plasma metabolomes as potential mediators. Analyzing causal mediation in such high-dimensional omics data presents substantial challenges, including complex dependencies among mediators and the need for advanced regularization or Bayesian techniques to ensure stable and interpretable estimation and selection of indirect effects. To this end, we propose a novel Bayesian framework for identifying active pathways and estimating indirect effects in the presence of high-dimensional multivariate mediators. Our approach adopts a multivariate stochastic search variable selection method, tailored for such complex mediation scenarios. Central to our method is the introduction of a set of priors for the selection: a Markov random field prior and sequential subsetting Bernoulli priors. The first prior's Markov property leverages the inherent correlations among mediators, thereby increasing power to detect mediated effects. The sequential subsetting aspect of the second prior encourages the simultaneous selection of relevant mediators and their corresponding indirect effects from the two model parts, providing a more coherent and efficient variable selection framework, specific to mediation analysis. Comprehensive simulation studies demonstrate that the proposed method provides superior power in detecting active mediating pathways. We further illustrate the practical utility of the method through its application to metabolome data from two cohort studies, highlighting its effectiveness in real data setting.

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引用次数: 0
Multi-IMPT: a biologically equivalent approach to proton ARC therapy. 多重impt:一种生物等效的质子ARC治疗方法。
Pub Date : 2024-11-26
Nimita Shinde, Yanan Zhu, Wei Wang, Wangyao Li, Yuting Lin, Gregory N Gan, Christopher Lominska, Ronny Rotondo, Ronald C Chen, Hao Gao

Objective: Proton spot-scanning arc therapy (ARC) is an emerging modality that can improve the high-dose conformity to targets compared with standard intensity-modulated proton therapy (IMPT). However, the efficient treatment delivery of ARC is challenging due to the required frequent energy changes during the continuous gantry rotation. This work proposes a novel method that delivers a multiple IMPT (multi-IMPT) plan that is equivalent to ARC in terms of biologically effective dose (BED).

Approach: The proposed multi-IMPT method utilizes a different subset of limited number of beam angles in each fraction for dose delivery. Due to the different dose delivered to organs at risk (OAR) in each fraction, we optimize biologically effective dose (BED) for OAR and the physical dose delivered for target in each fraction. The BED-based multi-IMPT inverse optimization problem is solved via the iterative convex relaxation method and the alternating direction method of multipliers. The effectiveness of the proposed multi-IMPT method is evaluated in terms of dose objectives in comparison with ARC.

Main results: Multi-IMPT provided similar plan quality with ARC. For example, multi-IMPT provided better OAR sparing and slightly better target dose coverage for the prostate case; similar dose distribution for the lung case; slightly worse dose coverage for the brain case; better dose coverage but slightly higher BED in OAR for the head-and-neck case.

Significance: We have proposed a multi-IMPT approach to deliver ARC-equivalent plan quality.

目的:与标准调强质子治疗(IMPT)相比,质子点扫描电弧治疗(arc)是一种提高高剂量靶一致性的新兴治疗方式。然而,由于在连续的龙门旋转过程中需要频繁的能量变化,ARC的有效治疗递送是具有挑战性的。这项工作提出了一种新的方法,提供了一个多重IMPT(多IMPT)计划,在生物有效剂量(BED)方面相当于ARC。方法:提出的多重impt方法利用每个分数中有限数量的光束角度的不同子集进行剂量递送。由于每个部分中传递到危险器官(OAR)的剂量不同,我们优化了OAR的生物有效剂量(BED)和每个部分中传递给目标的物理剂量。采用迭代凸松弛法和乘法器交替方向法求解基于bed的多impt逆优化问题。与ARC相比,根据剂量物镜评估了所提出的多重impt方法的有效性。主要结果:Multi-IMPT与ARC方案质量相近。例如,对于前列腺病例,多次impt提供了更好的OAR保留和略好的靶剂量覆盖;肺部病例的剂量分布相似;脑部病例的剂量覆盖率略差;头颈部病例的剂量覆盖率较好,但在OAR中BED略高。意义:我们提出了一种多impt方法来交付arc等效计划质量。
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引用次数: 0
JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data. JESTR:为非目标代谢组学数据注释候选分子排序的联合嵌入空间技术。
Pub Date : 2024-11-25
Apurva Kalia, Dilip Krishnan, Soha Hassoun

Motivation: A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra and in spectra-to-molecular fingerprint prediction (FP), annotation rates remain low.

Results: We introduce in this paper a novel paradigm (JESTR) for annotation. Unlike prior approaches that explicitly construct molecular fingerprints or spectra, JESTR leverages the insight that molecules and their corresponding spectra are views of the same data and effectively embeds their representations in a joint space. Candidate structures are ranked based on cosine similarity between the embeddings of query spectrum and each candidate. We evaluate JESTR against mol-to-spec and spec-to-FP annotation tools on three datasets. On average, for rank@[1-5], JESTR outperforms other tools by 23.6% - 71.6%. We further demonstrate the strong value of regularization with candidate molecules during training, boosting rank@1 performance by 11.4% and enhancing the model's ability to discern between target and candidate molecules. Through JESTR, we offer a novel promising avenue towards accurate annotation, therefore unlocking valuable insights into the metabolome.

Availability: Code and dataset available at https://github.com/HassounLab/JESTR1/.

动机代谢组学的一大挑战是标注:为质谱碎片模式分配分子结构。尽管最近在分子到光谱和光谱到分子指纹预测(FP)方面取得了进展,但注释率仍然很低:我们在本文中介绍了一种新的注释范式(JESTR)。与之前明确构建分子指纹或光谱的方法不同,JESTR 充分利用了分子及其相应光谱是同一数据的视图这一观点,并有效地将它们嵌入到一个联合空间中。候选结构根据查询光谱和每个候选结构的嵌入之间的余弦相似度进行排序。我们在三个数据集上对 JESTR 与 mol-to-spec 和 spec-toFP 注释工具进行了评估。平均而言,对于 rank@[1-5],JESTR 优于其他工具 23.6%-71.6%。我们进一步证明了在训练过程中对候选分子进行正则化的强大价值,将 rank@1 的性能提高了 11.4%,并增强了模型辨别目标分子和候选分子的能力。通过 JESTR,我们为实现精确注释提供了一种新的有前途的途径,从而开启了对代谢组的宝贵洞察。
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引用次数: 0
Improving Deformable Image Registration Accuracy through a Hybrid Similarity Metric and CycleGAN Based Auto-Segmentation. 基于相似度度量和CycleGAN的混合自动分割提高可变形图像配准精度。
Pub Date : 2024-11-25
Keyur D Shah, James A Shackleford, Nagarajan Kandasamy, Gregory C Sharp

Purpose: Deformable image registration (DIR) plays a critical role in adaptive radiation therapy (ART) to accommodate anatomical changes. However, conventional intensity-based DIR methods face challenges when registering images with unequal image intensities. In these cases, DIR accuracy can be improved using a hybrid image similarity metric which matches both image intensities and the location of known structures. This study aims to assess DIR accuracy using a hybrid similarity metric and leveraging CycleGAN-based intensity correction and auto-segmentation and comparing performance across three DIR workflows.

Methods: The proposed approach incorporates a hybrid image similarity metric combining a point-to-distance (PD) score and intensity similarity score. Synthetic CT (sCT) images were generated using a 2D CycleGAN model trained on unpaired CT and CBCT images, improving soft-tissue contrast in CBCT images. The performance of the approach was evaluated by comparing three DIR workflows: (1) traditional intensity-based (No PD), (2) auto-segmented contours on sCT (CycleGAN PD), and (3) expert manual contours (Expert PD). A 3D U-Net model was then trained on two datasets comprising 56 3D images and validated on 14 independent cases to segment the prostate, bladder, and rectum. DIR accuracy was assessed using Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD), and fiducial separation metrics.

Results: The hybrid similarity metric significantly improved DIR accuracy. For the prostate, DSC increased from 0.61 ± 0.18 (No PD) to 0.82 ± 0.13 (CycleGAN PD) and 0.89 ± 0.05 (Expert PD), with corresponding reductions in 95% HD from 11.75 mm to 4.86 mm and 3.27 mm, respectively. Fiducial separation was also reduced from 8.95 mm to 4.07 mm (CycleGAN PD) and 4.11 mm (Expert PD) (p < 0.05). Improvements in alignment were also observed for the bladder and rectum, highlighting the method's robustness.

Conclusion: A hybrid similarity metric that uses CycleGAN-based auto-segmentation presents a promising avenue for advancing DIR accuracy in ART. The study's findings suggest the potential for substantial enhancements in DIR accuracy by combining AI-based image correction and auto-segmentation with classical DIR.

目的:形变图像配准(DIR)在适应性放射治疗(ART)中至关重要,可以解释解剖变化。当图像强度不同时,传统的基于强度的DIR方法往往会失败。本研究评估了结合强度和结构信息的混合相似性度量,利用基于cyclegan的强度校正和跨三个DIR工作流程的自动分割。方法:采用点距离(PD)评分和强度相似度相结合的混合相似度度量法。合成CT (sCT)图像是使用未配对CT和CBCT图像训练的2D CycleGAN模型生成的,以增强软组织对比度。比较的DIR工作流程包括:(1)传统的基于强度的(No PD), (2) sCT上的自动分割轮廓(CycleGAN PD)和(3)专家手动轮廓(expert PD)。三维U-Net模型在56张图像上进行训练,并在14例病例上进行了验证。使用Dice Similarity Coefficient (DSC)、95% Hausdorff Distance (HD)和基准分离来评估DIR的准确性。结果:混合指标提高了DIR的准确性。对于前列腺,DSC从0.61+/-0.18 (No PD)增加到0.82+/-0.13 (CycleGAN PD)和0.89+/-0.05 (Expert PD), 95% HD分别从11.75 mm减少到4.86 mm和3.27 mm。基准间距从8.95 mm降至4.07 mm (CycleGAN PD)和4.11 mm (Expert PD) (p < 0.05)。膀胱和直肠也有改善。结论:本研究表明,使用基于cyclegan的混合相似度度量可以提高DIR的准确性,特别是对于低对比度的CBCT图像。这些发现强调了将基于人工智能的图像校正和分割整合到ART工作流程中以提高精度和简化临床流程的潜力。
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引用次数: 0
Deciphering genomic codes using advanced NLP techniques: a scoping review. 使用先进的自然语言处理技术破译基因组密码:范围回顾。
Pub Date : 2024-11-25
Shuyan Cheng, Yishu Wei, Yiliang Zhou, Zihan Xu, Drew N Wright, Jinze Liu, Yifan Peng

Objectives: The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of Natural Language Processing (NLP) techniques, particularly Large Language Models (LLMs) and transformer architectures, in deciphering genomic codes, focusing on tokenization, transformer models, and regulatory annotation prediction. This review aims to assess data and model accessibility in the most recent literature, gaining a better understanding of the existing capabilities and constraints of these tools in processing genomic sequencing data.

Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, our scoping review was conducted across PubMed, Medline, Scopus, Web of Science, Embase, and ACM Digital Library. Studies were included if they focused on NLP methodologies applied to genomic sequencing data analysis, without restrictions on publication date or article type.

Results: A total of 26 studies published between 2021 and April 2024 were selected for review. The review highlights that tokenization and transformer models enhance the processing and understanding of genomic data, with applications in predicting regulatory annotations like transcription-factor binding sites and chromatin accessibility.

Discussion: The application of NLP and LLMs to genomic sequencing data interpretation is a promising field that can help streamline the processing of large-scale genomic data while providing a better understanding of its complex structures. It can potentially drive advancements in personalized medicine by offering more efficient and scalable solutions for genomic analysis. Further research is needed to discuss and overcome limitations, enhancing model transparency and applicability.

目的:人类基因组测序数据的庞大和复杂的性质提出了有效分析的挑战。本文旨在研究自然语言处理(NLP)技术,特别是大型语言模型(llm)和转换器架构在破译基因组密码中的应用,重点是标记化、转换器模型和监管注释预测。本综述的目的是评估最新文献中的数据和模型可及性,更好地了解这些工具在处理基因组测序数据方面的现有能力和限制。方法:根据系统评价和荟萃分析(PRISMA)指南的首选报告项目,我们的范围审查在PubMed, Medline, Scopus, Web of Science, Embase和ACM数字图书馆进行。如果研究集中于应用于基因组测序数据分析的自然语言处理方法,则不受发表日期或文章类型的限制。结果:共选择了2021年至2024年4月期间发表的26项研究进行综述。这篇综述强调了标记化和转换模型增强了对基因组数据的处理和理解,在预测转录因子结合位点和染色质可及性等调控注释方面的应用。讨论:NLP和llm在基因组测序数据解释中的应用是一个很有前途的领域,可以帮助简化大规模基因组数据的处理,同时也提供了对其复杂结构的更好理解。它有潜力通过提供更有效和可扩展的基因组分析解决方案来推动个性化医疗的进步。还需要进一步的研究来讨论和克服现有的局限性,提高模型的透明度和适用性。
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引用次数: 0
Exploring Discrete Flow Matching for 3D De Novo Molecule Generation. 探索三维从头分子生成的离散流匹配。
Pub Date : 2024-11-25
Ian Dunn, David Ryan Koes

Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Flow matching is a recently proposed generative modeling framework that has achieved impressive performance on a variety of tasks including those on biomolecular structures. The seminal flow matching framework was developed only for continuous data. However, de novo molecular design tasks require generating discrete data such as atomic elements or sequences of amino acid residues. Several discrete flow matching methods have been proposed recently to address this gap. In this work we benchmark the performance of existing discrete flow matching methods for 3D de novo small molecule generation and provide explanations of their differing behavior. As a result we present FlowMol-CTMC, an open-source model that achieves state of the art performance for 3D de novo design with fewer learnable parameters than existing methods. Additionally, we propose the use of metrics that capture molecule quality beyond local chemical valency constraints and towards higher-order structural motifs. These metrics show that even though basic constraints are satisfied, the models tend to produce unusual and potentially problematic functional groups outside of the training data distribution. Code and trained models for reproducing this work are available at https://github.com/dunni3/FlowMol.

产生新分子结构的深层生成模型有可能促进化学发现。流匹配是最近提出的一种生成建模框架,在包括生物分子结构在内的各种任务中取得了令人印象深刻的表现。种子流量匹配框架仅针对连续数据开发。然而,从头开始的分子设计任务需要生成离散的数据,如原子元素或氨基酸残基序列。最近提出了几种离散流匹配方法来解决这一差距。在这项工作中,我们对现有的3D从头小分子生成离散流匹配方法的性能进行了基准测试,并提供了它们不同行为的解释。因此,我们提出了FlowMol-CTMC,这是一个开源模型,可以实现3D从头设计的最先进性能,比现有方法具有更少的可学习参数。此外,我们建议使用超越局部化学价约束和更高阶结构基序的指标来捕获分子质量。这些度量表明,即使基本约束得到满足,这些模型也倾向于在训练数据分布之外产生不寻常的和潜在的有问题的功能组。用于复制此工作的代码和经过训练的模型可在url{https://github.com/dunni3/FlowMol}上获得。
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引用次数: 0
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