GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction

Abstract

With the recent advancement of deep learning, molecular representation learning-automating the discovery of feature representation of molecular structure, has attracted significant attention from both chemists and machine learning researchers. Deep learning can facilitate a variety of downstream applications, including bio-property prediction, chemical reaction prediction, etc. Despite the fact that current SMILES string or molecular graph molecular representation learning algorithms (via sequence modeling and graph neural networks, respectively) have achieved promising results, there is no work to integrate the capabilities of both approaches in preserving molecular characteristics (e.g, atomic cluster, chemical bond) for further improvement. In this paper, we propose GraSeq, a joint graph and sequence representation learning model for molecular property prediction. Specifically, GraSeq makes a complementary combination of graph neural networks and recurrent neural networks for modeling two types of molecular inputs, respectively. In addition, it is trained by the multitask loss of unsupervised reconstruction and various downstream tasks, using limited size of labeled datasets. In a variety of chemical property prediction tests, we demonstrate that our GraSeq model achieves better performance than state-of-the-art approaches.

Few-Shot Graph Learning for Molecular Property Prediction

“…GNNs have showed attractive performance in various applications, such as recommendation systems [4,24], behavior modeling [38], and anomaly detection [44]. Molecular property prediction is also a popular application of GNNs since a molecule could be represented as a topological graph by treating atoms as nodes, and bonds as edges [7,8,18,20]. We will elaborate them in the next paragraph.…”

“…For the first group of methods, each molecule is represented as a graph associated with different atom nodes interconnected by bond edges. One typical way is to employ graph neural networks to learn molecular representations [7,8,10,18,20]. For example, Lu et al [18] proposed a novel hierarchical GNN.…”

“…Zheng et al [45] presented a new model to study structure-property relationships through a self-attentive linear notation syntax analysis. Guo et al [8] proposed a novel graph and sequence fusion learning model to capture information both from the molecular graph structure and SMILES. Here, we take each molecule as a graph as it preserves the molecular inner structure better, and employ graph neural networks to learn their representations.…”

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications

“…However, SMILES does not optimally preserve the molecular structure [31]. Thus, the first bridge works [32], [33] combined different molecular fingerprints and molecular graphs as input. Recently a group of models were introduced that use the molecular graph as input [31], [34], [35].…”

A Novel Deep Neural Network Technique for Drug–Target Interaction

“…The word2vec model [ 30 ] was used to represent the embedding of genes [ 31 ]. Graph neural networks (GNNs) and Bi-LSTM [ 32 ] were used to propose a graph and sequence fusion learning model that captures significant information from both a SMILES sequence and a molecular graph [ 33 ]. Four text-based information sources, namely, the protein sequence, ligand SMILES, protein domains and motifs, and maximum common substructure words, were used to predict binding affinity [ 34 ].…”