Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction

• URL: http://arxiv.org/abs/2205.11279v1
• Date: Mon, 23 May 2022 12:56:12 GMT
• Title: Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction
• Authors: Kuangqi Zhou, Kaixin Wang, Jiashi Feng, Jian Tang, Tingyang Xu, Xinchao Wang
• Abstract summary: How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery. To reduce annotation cost, deep Active Learning methods are developed to select only the most representative and informative data for annotating. We propose a Task-type-generic active learning framework (termed Tyger) that is able to handle different types of learning tasks in a unified manner.
• Score: 121.97742787439546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to accurately predict the properties of molecules is an essential problem in AI-driven drug discovery, which generally requires a large amount of annotation for training deep learning models. Annotating molecules, however, is quite costly because it requires lab experiments conducted by experts. To reduce annotation cost, deep Active Learning (AL) methods are developed to select only the most representative and informative data for annotating. However, existing best deep AL methods are mostly developed for a single type of learning task (e.g., single-label classification), and hence may not perform well in molecular property prediction that involves various task types. In this paper, we propose a Task-type-generic active learning framework (termed Tyger) that is able to handle different types of learning tasks in a unified manner. The key is to learn a chemically-meaningful embedding space and perform active selection fully based on the embeddings, instead of relying on task-type-specific heuristics (e.g., class-wise prediction probability) as done in existing works. Specifically, for learning the embedding space, we instantiate a querying module that learns to translate molecule graphs into corresponding SMILES strings. Furthermore, to ensure that samples selected from the space are both representative and informative, we propose to shape the embedding space by two learning objectives, one based on domain knowledge and the other leveraging feedback from the task learner (i.e., model that performs the learning task at hand). We conduct extensive experiments on benchmark datasets of different task types. Experimental results show that Tyger consistently achieves high AL performance on molecular property prediction, outperforming baselines by a large margin. We also perform ablative experiments to verify the effectiveness of each component in Tyger.

Related papers

• Active Learning to Guide Labeling Efforts for Question Difficulty Estimation [1.0514231683620516]
  Transformer-based neural networks achieve state-of-the-art performance, primarily through supervised methods but with an isolated study in unsupervised learning. This work bridges the research gap by exploring active learning for QDE, a supervised human-in-the-loop approach. Experiments demonstrate that active learning with PowerVariance acquisition achieves a performance close to fully supervised models after labeling only 10% of the training data.
  arXiv  Detail & Related papers  (2024-09-14T02:02:42Z)
• Learning Invariant Molecular Representation in Latent Discrete Space [52.13724532622099]
  We propose a new framework for learning molecular representations that exhibit invariance and robustness against distribution shifts. Our model achieves stronger generalization against state-of-the-art baselines in the presence of various distribution shifts.
  arXiv  Detail & Related papers  (2023-10-22T04:06:44Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation [7.856339385917824]
  This paper proposes Test-time Augmentation for Active Learning (TAAL), a novel semi-supervised active learning approach for segmentation. Our results on a publicly-available dataset of cardiac images show that TAAL outperforms existing baseline methods in both fully-supervised and semi-supervised settings.
  arXiv  Detail & Related papers  (2023-01-16T22:19:41Z)
• Structured Multi-task Learning for Molecular Property Prediction [30.77287550003828]
  We study multi-task learning for molecular property prediction in a novel setting, where a relation graph between tasks is available. In the emphlatent space, we model the task representations by applying a state graph neural network (SGNN) on the relation graph. In the emphoutput space, we employ structured prediction with the energy-based model (EBM), which can be efficiently trained through noise-contrastive estimation (NCE) approach.
  arXiv  Detail & Related papers  (2022-02-22T20:31:23Z)
• Improving VAE based molecular representations for compound property prediction [0.0]
  We propose a simple method to improve chemical property prediction performance of machine learning models. We show the relation between the performance of property prediction models and the distance between property prediction dataset and the larger unlabeled dataset.
  arXiv  Detail & Related papers  (2022-01-13T12:57:11Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Learning Purified Feature Representations from Task-irrelevant Labels [18.967445416679624]
  We propose a novel learning framework called PurifiedLearning to exploit task-irrelevant features extracted from task-irrelevant labels. Our work is built on solid theoretical analysis and extensive experiments, which demonstrate the effectiveness of PurifiedLearning.
  arXiv  Detail & Related papers  (2021-02-22T12:50:49Z)
• 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)
• Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
  We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training. We experimentally verify that the new dataset can significantly improve the ability of the learned FER model. To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
  arXiv  Detail & Related papers  (2020-05-18T09:36:51Z)

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.