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Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry (2401.03369v2)

Published 7 Jan 2024 in q-bio.MN, cs.LG, and q-bio.BM

Abstract: Molecular property prediction refers to the task of labeling molecules with some biochemical properties, playing a pivotal role in the drug discovery and design process. Recently, with the advancement of machine learning, deep learning-based molecular property prediction has emerged as a solution to the resource-intensive nature of traditional methods, garnering significant attention. Among them, molecular representation learning is the key factor for molecular property prediction performance. And there are lots of sequence-based, graph-based, and geometry-based methods that have been proposed. However, the majority of existing studies focus solely on one modality for learning molecular representations, failing to comprehensively capture molecular characteristics and information. In this paper, a novel multi-modal representation learning model, which integrates the sequence, graph, and geometry characteristics, is proposed for molecular property prediction, called SGGRL. Specifically, we design a fusion layer to fusion the representation of different modalities. Furthermore, to ensure consistency across modalities, SGGRL is trained to maximize the similarity of representations for the same molecule while minimizing similarity for different molecules. To verify the effectiveness of SGGRL, seven molecular datasets, and several baselines are used for evaluation and comparison. The experimental results demonstrate that SGGRL consistently outperforms the baselines in most cases. This further underscores the capability of SGGRL to comprehensively capture molecular information. Overall, the proposed SGGRL model showcases its potential to revolutionize molecular property prediction by leveraging multi-modal representation learning to extract diverse and comprehensive molecular insights. Our code is released at https://github.com/Vencent-Won/SGGRL.

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Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry

The paper "Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry" introduces a novel approach to molecular property prediction. This approach leverages multi-modal representation learning techniques that utilize sequence, graph, and geometry modalities, aiming to enhance the precision of molecular property prediction tasks. The paper's proposal, SGGRL, is a next-step evolution in the landscape of molecular representation learning, addressing the limitations of single-modality models by capturing a more comprehensive set of molecular characteristics.

The research places its foundation on the premise that each modality—the molecular sequence (such as SMILES strings), molecular graph (depicting atom connectivity), and geometry (3D molecular structures)—captures distinct aspects of molecular data which are critical for accurate prediction of biochemical properties. This paper argues that models focusing on single modalities often miss out on integrating valuable complementary information available in other forms, which could enhance the predictive performance.

Methodological Approach

SGGRL introduces a robust architecture composed of a sequence encoder, graph encoder, and geometry encoder. Each module is specially tailored for a specific modality:

  • Sequence Encoder: Leveraging a Bi-LSTM to preprocess SMILES sequences ensures the model effectively captures bidirectional context which is essential in dealing with the non-directionality of SMILES.
  • Graph Encoder: Based on Communicative Message Passing Neural Networks (CMPNN), this component utilizes the atom connectivity information to extract meaningful representations, emphasizing the role of structural topology.
  • Geometry Encoder: Employing the GEMGNN architecture, this module taps into the 3D geometrical conformation of molecules, augmenting the representation with detailed spatial configurations.

A salient feature of the SGGRL is the utilization of a GlobalAttentionPool layer to amalgamate representations from different modalities, followed by a fusion layer that combines them into a joint representation. This enables the capturing of comprehensive information while addressing data redundancy issues.

Furthermore, SGGRL employs a contrastive learning mechanism, NT-Xent Loss, across different modalities to maintain consistency and compatibility of feature spaces. This design choice ensures that the model captures universal molecular features that are congruent across modalities, avoiding the pitfalls of modality-specific biases.

Experimental Results

Empirical evaluations conducted on seven benchmark datasets demonstrate that SGGRL holds significant advantages over existing state-of-the-art models, particularly in both classification and regression tasks associated with molecular property prediction. The comparative analysis shows that SGGRL consistently outperforms popular models like GraSeq, 3D Infomax, and GraphMVP across different datasets, including small-scale tasks where comprehensive representation becomes more crucial.

Implications and Future Directions

The implications of SGGRL are manifold, suggesting that multi-modal integration could be a pivotal factor in advancing molecular property prediction tasks. By providing a more holistic view of molecular structures and properties, SGGRL could significantly impact drug discovery and biochemical research, potentially streamlining processes and reducing costs.

Future work could focus on refining each of the modal encoders to incorporate more advanced machine learning models that can capture intricate modality-specific features more effectively. Additionally, further research could explore real-time applications within biochemical simulations or predictive modeling in medicinal chemistry, extending the utility of SGGRL beyond the datasets analyzed.

In summary, this paper presents a compelling case for multi-modal representation learning in molecular property prediction, highlighting the potential of integrating sequence, graph, and geometry data to achieve superior predictive accuracy. The approach could redefine approaches in bioinformatics, opening new avenues for research and application in artificial intelligence-driven drug discovery.

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Authors (4)
  1. Zeyu Wang (137 papers)
  2. Tianyi Jiang (5 papers)
  3. Jinhuan Wang (27 papers)
  4. Qi Xuan (113 papers)
Citations (5)
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