Artificial Intelligence predicts drug properties by encoding drug molecules,
aiding in the rapid screening of candidates. Different molecular
representations, such as SMILES and molecule graphs, contain complementary
information for molecular encoding. Thus exploiting complementary information
from different molecular representations is one of the research priorities in
molecular encoding. Most existing methods for combining molecular
multi-modalities only use molecular-level information, making it hard to encode
intra-molecular alignment information between different modalities. To address
this issue, we propose a multi-granularity fusion method that is MolFusion. The
proposed MolFusion consists of two key components: (1) MolSim, a
molecular-level encoding component that achieves molecular-level alignment
between different molecular representations. and (2) AtomAlign, an atomic-level
encoding component that achieves atomic-level alignment between different
molecular representations. Experimental results show that MolFusion effectively
utilizes complementary multimodal information, leading to significant
improvements in performance across various classification and regression tasks.

人工智能通过编码药物分子来预测药物特性，帮助快速筛选候选药物。利用不同来源的分子表示，如 SMILES 和分子图，可以提供互补信息进行分子编码。我们提出了一种多粒度融合方法 MolFusion，该方法利用分子级和原子级编码实现不同分子表示之间的相互对齐，实验结果表明 MolFusion 有效地利用了多模态的互补信息，在不同分类和回归任务中显著提高了性能。

MolFusion: 分子表示的多模态融合学习 -- 基于多粒度视图

MolFusion: Multimodal Fusion Learning for Molecular Representations via  Multi-granularity Views

Predicting drug properties is key in drug discovery to enable de-risking of
assets before expensive clinical trials, and to find highly active compounds
faster. Interest from the Machine Learning community has led to the release of
a variety of benchmark datasets and proposed methods. However, it remains
unclear for practitioners which method or approach is most suitable, as
different papers benchmark on different datasets and methods, leading to
varying conclusions that are not easily compared. Our large-scale empirical
study links together numerous earlier works on different datasets and methods;
thus offering a comprehensive overview of the existing property classes,
datasets, and their interactions with different methods. We emphasise the
importance of uncertainty quantification and the time and therefore cost of
applying these methods in the drug development decision-making cycle. We
discover that the best method depends on the dataset, and that engineered
features with classical ML methods often outperform deep learning.
Specifically, QSAR datasets are typically best analysed with classical methods
such as Gaussian Processes while ADMET datasets are sometimes better described
by Trees or Deep Learning methods such as Graph Neural Networks or language
models. Our work highlights that practitioners do not yet have a
straightforward, black-box procedure to rely on, and sets the precedent for
creating practitioner-relevant benchmarks. Deep learning approaches must be
proven on these benchmarks to become the practical method of choice in drug
property prediction.

预测药物特性在药物发现中是关键，以便在昂贵的临床试验之前减少风险，并更快地找到高活性化合物。尽管机器学习社区对此表现出了兴趣并提出了各种标准数据集和方法，然而对于从业人员而言，目前仍不清楚哪种方法或途径最适用，因为不同的论文以不同的数据集和方法进行基准测试，导致得出的结论各不相同且难以比较。我们的大规模实证研究将不同数据集和方法的众多相关作品联系到一起，从而全面概述了现有的属性类别、数据集及其与不同方法的相互作用。我们强调不确定性量化以及应用这些方法在药物开发决策周期中的时间和成本的重要性。我们发现最佳方法取决于数据集，并且具备经典机器学习方法的工程特征通常优于深度学习。具体而言，经典方法（如高斯过程）通常最适合分析 QSAR 数据集，而树或深度学习方法（如图神经网络或语言模型）有时更适合描述 ADMET 数据集。我们的研究强调了从业人员尚无可靠的、直接可行的程序可供依赖，并为创建与从业人员相关的基准测试案例奠定了先例。深度学习方法必须在这些基准测试案例中得到验证才能成为药物特性预测的实际选择方法。

现实世界中药物属性预测的当前方法

Current Methods for Drug Property Prediction in the Real World

Structure-based models in the molecular sciences can be highly sensitive to
input geometries and give predictions with large variance under subtle
coordinate perturbations. We present an approach to mitigate this failure mode
by generating conformations that explicitly minimize uncertainty in a
predictive model. To achieve this, we compute differentiable estimates of
aleatoric \textit{and} epistemic uncertainties directly from learned
embeddings. We then train an optimizer that iteratively samples embeddings to
reduce these uncertainties according to their gradients. As our predictive
model is constructed as a variational autoencoder, the new embeddings can be
decoded to their corresponding inputs, which we call \textit{MoleCLUEs}, or
(molecular) counterfactual latent uncertainty explanations
\citep{antoran2020getting}. We provide results of our algorithm for the task of
predicting drug properties with maximum confidence as well as analysis of the
differentiable structure simulations.

本文提出了一种减少预测模型中不确定性的方法，即通过计算学习嵌入的 Aleatoric 和 Epistemic 不确定性的可微估计值，并训练一个优化器，迭代地从中减少这些不确定性，以生成一种名为 MoleCLUEs 的新嵌入，同时提供了在预测药物属性方面的结果和不同 iable 结构模拟的分析。