KPGT

Li, Han; Zhao, Dan; Zeng, Jianyang

doi:10.1145/3534678.3539426

Cited by 27 publications

(19 citation statements)

References 19 publications

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“…It was found that initializing the parameters of TranSiGen using perturbational profiles by gene knockdown leads to better performance compared to random initialization. Additionally, the pre-training representation using Knowledge-guided Pretraining of Graph Transformer (KPGT) 17 further enhances the performance of inferring DEGs, surpassing the molecular fingerprint ECFP4 (Supplementary Table 1).…”

Section: Resultsmentioning

confidence: 99%

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TranSiGen: Deep representation learning of chemical-induced transcriptional profile

Tong,

Qu,

Kong

et al. 2023

Preprint

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With the advancement of high-throughput RNA sequencing technologies, the use of chemical-induced transcriptional profiling has greatly increased in biomedical research. However, the usefulness of transcriptomics data is limited by inherent random noise and technical artefacts that may cause systematical biases. These limitations make it challenging to identify the true signal of perturbation and extract knowledge from the data. In this study, we propose a deep generative model called Transcriptional Signatures Generator (TranSiGen), which aims to denoise and reconstruct transcriptional profiles through self-supervised representation learning.TranSiGen uses cell basal gene expression and compound molecular structure representation to infer the chemical-induced transcriptional profile. Results demonstrate the effectiveness of TranSiGen in learning and predicting differential expression genes. The representation derived from TranSiGen can also serve as an alternative phenotype information, with applications in ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. We envisage that integrating TranSiGen into the drug discovery and mechanism research pipeline will promote the development of biomedicine.

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Section: Resultsmentioning

confidence: 99%

“…Considering that the current number of compounds with experimentally measured gene expression profiles is still limited compared to the vast chemical space, TranSiGen utilized the pre-trained molecular representation KPGT 17 for compounds. KPGT is a novel self-supervised learning framework for molecular graph representation.…”

Section: Methodsmentioning

confidence: 99%

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

Tong,

Qu,

Kong

et al. 2023

Preprint

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“…Last but not least, self‐supervised learning has gained interest in predicting properties of organic molecules, [76] drugs, [77] proteins, [77a,78] and polymers [79] . Although use in electrochemical material discovery is just beginning since pre‐trained models can be trained with free unlabeled data and reused for various chemistry‐related tasks, it could help in the future design of useful materials.…”

Section: Discussionmentioning

confidence: 99%

Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery

Boonpalit,

Kinchagawat,

Namuangruk

2024

ChemElectroChem

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Machine learning has gained considerable attention in the material science domain and helped discover advanced materials for electrochemical applications. Numerous studies have demonstrated its potential to reduce the resources required for material screening. However, a significant proportion of these studies have adopted a supervised learning approach, which entails the laborious task of constructing random training databases and does not always ensure the model‘s reliability while screening unseen materials. Herein, we evaluate the limitations of supervised machine learning from the perspective of the applicability domain. The applicability domain of a model is the region in chemical space where the structure‐property relationship is covered by the training set so that the model can give reliable predictions. We review methods that have been developed to overcome such limitations, such as the active learning framework and self‐supervised learning.

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“…Based on the prominent results of ActFound on all settings we have evaluated, we hypothesize that it can be further improved by incorporating other pre-trained methods. We plan to incorporate pre-trained methods, such as KPGT, 48 GROVER 49 and MOLFORMER 50 to further improve ActFound.…”

Section: Discussionmentioning

confidence: 99%

A foundation model for bioactivity prediction using pairwise meta-learning

Feng,

Liu,

Huang

et al. 2023

Preprint

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Compound bioactivity plays an important role in different stages of drug development and discovery. Existing machine learning approaches have poor generalization ability in compound bioactivity prediction due to the small number of compounds in each assay and incompatible measurements among assays. Here, we propose ActFound, a foundation model for bioactivity prediction trained on 2.3 million experimentally-measured bioactivity compounds and 50, 869 assays from ChEMBL and BindingDB. The key idea of ActFound is to employ pairwise learning to learn the relative value differences between two compounds within the same assay to circumvent the incompatibility among assays. ActFound further exploits meta-learning to jointly optimize the model from all assays. On six real-world bioactivity datasets, ActFound demonstrates accurate in-domain prediction and strong generalization across datasets, assay types, and molecular scaffolds. We also demonstrated that ActFound can be used as an accurate alternative to the leading computational chemistry software FEP+(OPLS4) by achieving comparable performance when only using a few data points for fine-tuning. The promising results of ActFound indicate that ActFound can be an effective foundation model for a wide range of tasks in compound bioactivity prediction, paving the path for machine learning-based drug development and discovery.

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KPGT

Cited by 27 publications

References 19 publications

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery

A foundation model for bioactivity prediction using pairwise meta-learning

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