We present a novel multimodal language model approach for predicting
molecular properties by combining chemical language representation with
physicochemical features. Our approach, MULTIMODAL-MOLFORMER, utilizes a causal
multistage feature selection method that identifies physicochemical features
based on their direct causal effect on a specific target property. These causal
features are then integrated with the vector space generated by molecular
embeddings from MOLFORMER. In particular, we employ Mordred descriptors as
physicochemical features and identify the Markov blanket of the target
property, which theoretically contains the most relevant features for accurate
prediction. Our results demonstrate a superior performance of our proposed
approach compared to existing state-of-the-art algorithms, including the
chemical language-based MOLFORMER and graph neural networks, in predicting
complex tasks such as biodegradability and PFAS toxicity estimation. Moreover,
we demonstrate the effectiveness of our feature selection method in reducing
the dimensionality of the Mordred feature space while maintaining or improving
the model's performance. Our approach opens up promising avenues for future
research in molecular property prediction by harnessing the synergistic
potential of both chemical language and physicochemical features, leading to
enhanced performance and advancements in the field.

本文介绍了一种新颖的多模态语言模型方法，结合化学语言表征和物理化学特征，以预测分子性质。我们的方法使用因果多阶段特征选择方法，在 MOLFORMER 生成的分子嵌入向量空间中，将这些因果特征与物理化学特征相结合。我们的结果表明，与现有的化学语言模型 MOLFORMER 和图形神经网络相比，在预测生物降解性和 PFAS 毒性估计等复杂任务方面，我们的提出的方法表现更优秀。此外，我们证明了我们的特征选择方法可以在保持或提高模型性能的同时降低 Mordred 特征空间的维数。这一方法为未来的分子性质预测研究开辟了有 promising avenues。