Beyond Chemical Language: A Multimodal Approach to Enhance Molecular
  Property Prediction
Eduardo Soares Emilio Vital Brazil Karen Fiorela Aquino Gutierrez Renato Cerqueira Dan Sanders Kristin Schmidt Dmitry Zubarev
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.