Manifold Regularized Transfer Distance Metric Learning

Hai-bo, Shi; Luo, Yong; Xu, Chao; Wen, Yonggang

doi:10.5244/c.29.158

Cited by 11 publications

(14 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use only 1,128 ESOL datasets (Delaney, 2004). We add 3D coordinates to pre-train datasets using the Merck molecular force field (MMFF94) (Halgren, 1996) function, which can obtain 3D coordinates faster (Stärk et al ., 2021) than the latest deep learning-based methods (Ganea et al ., 2021; Shi et al ., 2021).…”

Section: Methodsmentioning

confidence: 99%

3D Graph Contrastive Learning for Molecular Property Prediction

Moon

Kwon

2022

Preprint

View full text Add to dashboard Cite

Motivation: Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, but a few issues exist. (1) Existing SSL models are large-scale; there is a limitation to implementing SSL where the computing resource is insufficient. (2) In most cases, they do not utilize 3D structural information for molecular representation learning. The activity of a drug is closely related to the structure of the drug molecule. Nevertheless, most current models do not use 3D information or use it partially. (3) Previous models that apply contrastive learning to molecules use the augmentation of permuting atoms and bonds. Therefore, molecules having different characteristics can be in the same positive samples. We propose a novel contrastive learning framework, small-scale 3D Graph Contrastive Learning (3DGCL) for molecular property prediction, to solve the above problems. Results: 3DGCL learns the molecular representation by reflecting the molecular structure through the pre-training process that does not change the semantics of the drug. Using only 1,128 samples for pre-train data and 1 million model parameters, we achieved the state-of-the-art or comparable performance in four regression benchmark datasets. Extensive experiments demonstrate that 3D structural information based on chemical knowledge is essential to molecular representation learning for property prediction

show abstract

Section: Methodsmentioning

confidence: 99%

3D Graph Contrastive Learning for Molecular Property Prediction

Moon

Kwon

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Another advantage of the model is that the PSD constraint of the target metric can be automatically satisfied, and thus the computational cost is low. A semi-supervised extension was presented in [26] by combining it with manifold regularization.…”

Section: R(amentioning

confidence: 99%

Transfer Metric Learning: Algorithms, Applications and Outlooks

Luo,

Wen,

Duan

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label information (such as class labels or pair/triplet constraints) to achieve satisfactory performance. However, the label information may be insufficient in real-world applications due to the high-labeling cost, and DML may fail in this case. Transfer metric learning (TML) is able to mitigate this issue for DML in the domain of interest (target domain) by leveraging knowledge/information from other related domains (source domains). Although achieved a certain level of development, TML has limited success in various aspects such as selective transfer, theoretical understanding, handling complex data, big data and extreme cases. In this survey, we present a systematic review of the TML literature. In particular, we group TML into different categories according to different settings and metric transfer strategies, such as direct metric approximation, subspace approximation, distance approximation, and distribution approximation. A summarization and insightful discussion of the various TML approaches and their applications will be presented. Finally, we indicate some challenges and provide possible future directions.

show abstract

“…18,20 However, an important problem is that these representations are not invariant with the isometries of the space. Graph neural networks are widely and successfully exploited in molecular chemistry 31,32 and also in crystal property prediction 33−36 and do not suffer from this drawback. In addition, they can capture the local structure quite accurately.…”

Section: Introductionmentioning

confidence: 99%

“…The score-based generative models have recently been developed for the generation of 3D molecules. 31,32,40 1.2. Problem Definition.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells

Sultanov,

Crivello,

Rebafka

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition, by using machine learning generative models. Compared with the generation of molecules, crystal structures pose new difficulties arising from the periodic nature of the crystal and from the specific symmetry constraints related to the space group. In this work, score-based probabilistic models based on annealed Langevin dynamics, which have shown excellent performance in various applications, are adapted to the task of crystal generation. The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed. During the training of the model, the lattice is learned from the available data, whereas during the sampling of a new chemical structure, two denoising processes are used in parallel to generate the lattice along with the generation of the atomic positions. A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages and a better quality of the sampled structures. We show that our model is capable of generating new candidate structures in any chosen chemical system and crystal group without any additional training. To illustrate the functionality of the proposed method, a comparison of our model to other recent generative models based on descriptor-based metrics is provided.

show abstract

Manifold Regularized Transfer Distance Metric Learning

Cited by 11 publications

References 1 publication

3D Graph Contrastive Learning for Molecular Property Prediction

3D Graph Contrastive Learning for Molecular Property Prediction

Transfer Metric Learning: Algorithms, Applications and Outlooks

Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells

Contact Info

Product

Resources

About