Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction
Submission Track: Papers
Submission Category: Automated Material Characterization
Keywords: chemical language model, large transformer, foundation model, transformer, MoLFormer, liquid formulations, battery electrolytes
Supplementary Material: pdf
TL;DR: We propose a novel multimodal approach that uses a large language model (MoLFormer) trained to predict properties of battery electrolytes.
Abstract: 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 multi-stage 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.
Digital Discovery Special Issue: Yes
Submission Number: 8
Loading