The task of inferring three-dimensional molecular configurations from their
two-dimensional graph representations is of critical significance in the
domains of computational chemistry and the development of pharmaceuticals. It
contributes fundamentally to our grasp of molecular mechanisms and
interactions. The rapid evolution of machine learning, especially in the realm
of deep generative networks, has catalyzed breakthroughs in the precision of
such predictive modeling. Traditional methodologies typically employ a
bifurcated strategy: initially estimating interatomic distances followed by
sculpting the spatial molecular structure via solving a distance geometry
problem. This sequential approach, however, occasionally fails to capture the
intricacies of local atomic arrangements accurately, thus compromising the
integrity of the resultant structural models. Addressing these deficiencies,
this work introduces an avant-garde generative framework: \method{}, which is
predicated on the diffusion principles found in classical non-equilibrium
thermodynamics. \method{} envisages atoms as discrete entities and is adept at
guiding the reversal of diffusion morphing a distribution of stochastic noise
back into coherent molecular forms through a process akin to a Markov chain.
This transformation begins with the initial representation of a molecular graph
in an abstract latent space, progressing to the realization of the
three-dimensional forms via an elaborate bilevel optimization scheme, tailored
to respect the task's specific requirements.

借助深度生成网络，本文提出了一种新颖的生成框架 —— 方法，在计算化学和药物开发领域推动了从二维分子结构推断三维分子构型的精确性，通过一个马尔科夫链过程，将随机噪声的分布转化为连贯的分子形式，并在尊重特定要求的基础上通过一个精心设计的双层优化方案实现了分子的三维形式的实现。

扩散驱动的分子构象预测生成框架

Diffusion-Driven Generative Framework for Molecular Conformation  Prediction

BACKGROUND: The amount of biomedical literature is rapidly growing and it is
becoming increasingly difficult to keep manually curated knowledge bases and
ontologies up-to-date. In this study we applied the word2vec deep learning
toolkit to medical corpora to test its potential for identifying relationships
from unstructured text. We evaluated the efficiency of word2vec in identifying
properties of pharmaceuticals based on mid-sized, unstructured medical text
corpora available on the web. Properties included relationships to diseases
('may treat') or physiological processes ('has physiological effect'). We
compared the relationships identified by word2vec with manually curated
information from the National Drug File - Reference Terminology (NDF-RT)
ontology as a gold standard. RESULTS: Our results revealed a maximum accuracy
of 49.28% which suggests a limited ability of word2vec to capture linguistic
regularities on the collected medical corpora compared with other published
results. We were able to document the influence of different parameter settings
on result accuracy and found and unexpected trade-off between ranking quality
and accuracy. Pre-processing corpora to reduce syntactic variability proved to
be a good strategy for increasing the utility of the trained vector models.
CONCLUSIONS: Word2vec is a very efficient implementation for computing vector
representations and for its ability to identify relationships in textual data
without any prior domain knowledge. We found that the ranking and retrieved
results generated by word2vec were not of sufficient quality for automatic
population of knowledge bases and ontologies, but could serve as a starting
point for further manual curation.

本研究采用 Word2Vec 深度学习工具对医学语料库进行了测试，以验证其在未结构化文本中识别关系的潜力，结果表明 Word2Vec 的排名生成和检索结果质量不足以用于知识库自动填充，但可作为手动筛选的起点。