Learn molecular representations from large-scale unlabeled molecules for drug discovery

• URL: http://arxiv.org/abs/2012.11175v1
• Date: Mon, 21 Dec 2020 08:21:49 GMT
• Title: Learn molecular representations from large-scale unlabeled molecules for drug discovery
• Authors: Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen, Yihuan Yu, Xiaojun Yao, Peng Gao, Guotong Xie, Sen Song
• Abstract summary: Molecular Pre-training Graph-based deep learning framework, named MPG, leans molecular representations from large-scale unlabeled molecules. MolGNet can capture valuable chemistry insights to produce interpretable representation. MPG is promising to become a novel approach in the drug discovery pipeline.
• Score: 19.222413268610808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and have poor generalization capability. Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful MolGNet model and an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemistry insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction, and drug-target interaction, involving 13 benchmark datasets. Our work demonstrates that MPG is promising to become a novel approach in the drug discovery pipeline.

Related papers

• Pre-trained Molecular Language Models with Random Functional Group Masking [54.900360309677794]
  We propose a SMILES-based underlineem Molecular underlineem Language underlineem Model, which randomly masking SMILES subsequences corresponding to specific molecular atoms. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities.
  arXiv  Detail & Related papers  (2024-11-03T01:56:15Z)
• Molecular Property Prediction Based on Graph Structure Learning [29.516479802217205]
  We propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. With molecular fingerprints, we construct a molecular similarity graph (MSG)
  arXiv  Detail & Related papers  (2023-12-28T06:45:13Z)
• MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures [2.5563339057415218]
  MolIG is a novel MultiModaL molecular pre-training framework for predicting molecular properties based on Image and Graph structures. It amalgamates the strengths of both molecular representation forms. It exhibits enhanced performance in downstream tasks pertaining to molecular property prediction within benchmark groups.
  arXiv  Detail & Related papers  (2023-11-28T10:28:35Z)
• MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning [3.179128580341411]
  MolCAP is a graph pretraining Transformer based on chemical reactivity (IMR) knowledge with prompted finetuning. Prompted by MolCAP, even basic graph neural networks are capable of achieving surprising performance that outperforms previous models.
  arXiv  Detail & Related papers  (2023-06-13T13:48:06Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [55.42602325017405]
  We propose a novel method called GODE, which takes into account the two-level structure of individual molecules. By pre-training two graph neural networks (GNNs) on different graph structures, combined with contrastive learning, GODE fuses molecular structures with their corresponding knowledge graph substructures. When fine-tuned across 11 chemical property tasks, our model outperforms existing benchmarks, registering an average ROC-AUC uplift of 13.8% for classification tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning [77.31492888819935]
  We propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT) MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. Experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction.
  arXiv  Detail & Related papers  (2022-12-20T19:32:30Z)
• Retrieval-based Controllable Molecule Generation [63.44583084888342]
  We propose a new retrieval-based framework for controllable molecule generation. We use a small set of molecules to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning.
  arXiv  Detail & Related papers  (2022-08-23T17:01:16Z)
• Few-Shot Graph Learning for Molecular Property Prediction [46.60746023179724]
  We propose Meta-MGNN, a novel model for few-shot molecular property prediction. To exploit unlabeled molecular information, Meta-MGNN further incorporates molecular structure, attribute based self-supervised modules and self-attentive task weights. Extensive experiments on two public multi-property datasets demonstrate that Meta-MGNN outperforms a variety of state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-16T01:55:34Z)
• ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction [61.33144688400446]
  We propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules. In the teacher model, we propose a novel semi-supervised learning method to learn general representation that jointly exploits information from molecular structure and molecular distribution. At last, we proposed a novel active learning strategy in terms of molecular diversities to select informative data during the whole framework learning.
  arXiv  Detail & Related papers  (2020-07-07T04:22:39Z)
• Self-Supervised Graph Transformer on Large-Scale Molecular Data [73.3448373618865]
  We propose a novel framework, GROVER, for molecular representation learning. GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning.
  arXiv  Detail & Related papers  (2020-06-18T08:37:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.