Predicting the physico-chemical properties of pure substances and mixtures is
a central task in thermodynamics. Established prediction methods range from
fully physics-based ab-initio calculations, which are only feasible for very
simple systems, over descriptor-based methods that use some information on the
molecules to be modeled together with fitted model parameters (e.g.,
quantitative-structure-property relationship methods or classical group
contribution methods), to representation-learning methods, which may, in
extreme cases, completely ignore molecular descriptors and extrapolate only
from existing data on the property to be modeled (e.g., matrix completion
methods). In this work, we propose a general method for combining molecular
descriptors with representation learning using the so-called expectation
maximization algorithm from the probabilistic machine learning literature,
which uses uncertainty estimates to trade off between the two approaches. The
proposed hybrid model exploits chemical structure information using graph
neural networks, but it automatically detects cases where structure-based
predictions are unreliable, in which case it corrects them by
representation-learning based predictions that can better specialize to unusual
cases. The effectiveness of the proposed method is demonstrated using the
prediction of activity coefficients in binary mixtures as an example. The
results are compelling, as the method significantly improves predictive
accuracy over the current state of the art, showcasing its potential to advance
the prediction of physico-chemical properties in general.

利用分子描述符和表示学习相结合的方法，通过结合概率机器学习文献中的期望最大化算法，自动检测并纠正基于结构的预测不可靠的情况，从而显著提高了对二元混合物中活度系数的预测准确性，展示了在物理化学性质预测中的潜力。

平衡分子信息与实证数据在物理 - 化学性质预测中的应用

Balancing Molecular Information and Empirical Data in the Prediction of  Physico-Chemical Properties

In data-driven drug discovery, designing molecular descriptors is a very
important task. Deep generative models such as variational autoencoders (VAEs)
offer a potential solution by designing descriptors as probabilistic latent
vectors derived from molecular structures. These models can be trained on large
datasets, which have only molecular structures, and applied to transfer
learning. Nevertheless, the approximate posterior distribution of the latent
vectors of the usual VAE assumes a simple multivariate Gaussian distribution
with zero covariance, which may limit the performance of representing the
latent features. To overcome this limitation, we propose a novel molecular deep
generative model that incorporates a hierarchical structure into the
probabilistic latent vectors. We achieve this by a denoising diffusion
probabilistic model (DDPM). We demonstrate that our model can design effective
molecular latent vectors for molecular property prediction from some
experiments by small datasets on physical properties and activity. The results
highlight the superior prediction performance and robustness of our model
compared to existing approaches.

本文提出了一种新的分子深度生成模型，该模型在概率潜变量中加入分层结构，并能通过小数据集实现有效的分子潜在向量设计和物性预测。