Generative Flow Networks (GFlowNets) are probabilistic models predicated on
Markov flows, employing specific amortization algorithms to learn stochastic
policies that generate compositional substances including biomolecules,
chemical materials, and more. Demonstrating formidable prowess in generating
high-performance biochemical molecules, GFlowNets accelerate the discovery of
scientific substances, effectively circumventing the time-consuming,
labor-intensive, and costly shortcomings intrinsic to conventional material
discovery. However, previous work often struggles to accumulate exploratory
experience and is prone to becoming disoriented within expansive sampling
spaces. Attempts to address this issue, such as LS-GFN, are limited to local
greedy searches and lack broader global adjustments. This paper introduces a
novel GFlowNet variant, the Dynamic Backtracking GFN (DB-GFN), which enhances
the adaptability of decision-making steps through a reward-based dynamic
backtracking mechanism. DB-GFN permits backtracking during the network
construction process according to the current state's reward value, thus
correcting disadvantageous decisions and exploring alternative pathways during
the exploration process. Applied to generative tasks of biochemical molecules
and genetic material sequences, DB-GFN surpasses existing GFlowNet models and
traditional reinforcement learning methods in terms of sample quality,
exploration sample quantity, and training convergence speed. Furthermore, the
orthogonal nature of DB-GFN suggests its potential as a powerful tool for
future improvements in GFN networks, with the promise of integrating with other
strategies to achieve more efficient search performance.

利用动态回溯机制进行决策，该论文介绍了一种新颖的 GFlowNet 变种 —— 动态回溯 GFN（DB-GFN），用于生成生化分子和遗传材料序列的生成任务，其中 DB-GFN 在样本质量、探索样本数量和训练收敛速度方面超过现有的 GFlowNet 模型和传统强化学习方法，具有潜在的集成其他策略以实现更高效搜索性能的能力。

GFlowNet 中的动态回溯：通过奖励依赖调整机制增强决策步骤

Dynamic Backtracking in GFlowNet: Enhancing Decision Steps with  Reward-Dependent Adjustment Mechanisms

The dynamics of biomolecules are crucial for our understanding of their
functioning in living systems. However, current 3D imaging techniques, such as
cryogenic electron microscopy (cryo-EM), require freezing the sample, which
limits the observation of their conformational changes in real time. The
innovative liquid-phase electron microscopy (liquid-phase EM) technique allows
molecules to be placed in the native liquid environment, providing a unique
opportunity to observe their dynamics. In this paper, we propose TEMPOR, a
Temporal Electron MicroscoPy Object Reconstruction algorithm for liquid-phase
EM that leverages an implicit neural representation (INR) and a dynamical
variational auto-encoder (DVAE) to recover time series of molecular structures.
We demonstrate its advantages in recovering different motion dynamics from two
simulated datasets, 7bcq and Cas9. To our knowledge, our work is the first
attempt to directly recover 3D structures of a temporally-varying particle from
liquid-phase EM movies. It provides a promising new approach for studying
molecules' 3D dynamics in structural biology.

通过结合内隐神经表示（INR）和动态变分自编码器（DVAE），我们提出了一种名为 TEMPOR 的用于液相电子显微镜（liquid-phase EM）的时间电子显微镜物体重建算法，可以恢复分子结构的时间序列，从而直接观察分子的三维动态变化。这项研究为结构生物学中研究分子的三维动态变化提供了一种有前景的新方法。

从液相电子显微镜视频中恢复分子的三维动力学

Recovering a Molecule's 3D Dynamics from Liquid-phase Electron  Microscopy Movies

Cryogenic electron microscopy (cryo-EM) has transformed structural biology by
allowing to reconstruct 3D biomolecular structures up to near-atomic
resolution. However, the 3D reconstruction process remains challenging, as the
3D structures may exhibit substantial shape variations, while the 2D image
acquisition suffers from a low signal-to-noise ratio, requiring to acquire very
large datasets that are time-consuming to process. Current reconstruction
methods are precise but computationally expensive, or faster but lack a
physically-plausible model of large molecular shape variations. To fill this
gap, we propose CryoChains that encodes large deformations of biomolecules via
rigid body transformation of their polymer instances (chains), while
representing their finer shape variations with the normal mode analysis
framework of biophysics. Our synthetic data experiments on the human
$\text{GABA}_{\text{B}}$ and heat shock protein show that CryoChains gives a
biophysically-grounded quantification of the heterogeneous conformations of
biomolecules, while reconstructing their 3D molecular structures at an improved
resolution compared to the current fastest, interpretable deep learning method.

该研究提出了一种新的生物分子三维重建方法称为 CryoChains，可通过其聚合物实例（链）的刚体变换将生物分子的大变形量进行编码，同时使用生物物理的正常模分析框架表示其更细微的形状变化，可在比当前最快的可解释的深度学习方法更高分辨率地重建其三维分子结构。