MolFPG: Multi-level fingerprint-based Graph Transformer for accurate and robust drug toxicity prediction

Teng, Saisai; Yin, Chenglin; Wang, Yu; Chen, Xiandong; Yan, Zhongmin; Cui, Lizhen; Wei, Leyi

doi:10.1016/j.compbiomed.2023.106904

Cited by 13 publications

(1 citation statement)

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“…In the case of chronic kidney disease, GNN has been used to identify potential biomarkers that can help predict the progression of the disease and improve patient outcomes [117]. GNN can leverage dynamic graph convolutional networks with feature selection to extract highquality omic-specific embedding information, aiding biomarker discovery in drug development [139][140]. By analyzing large-scale patient data sets, GNN can identify patterns and relationships that can be missed by traditional statistical methods and provide a more comprehensive understanding of the underlying biology of the disease.…”

Section: Analysis and Interpretation Of Proposed Research Questionsmentioning

confidence: 99%

A Systematic Review of Graph Neural Network in Healthcare-Based Applications: Recent Advances, Trends, and Future Directions

Paul,

Saha,

Hasan

et al. 2024

IEEE Access

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Graph neural network (GNN) is a formidable deep learning framework that enables the analysis and modeling of intricate relationships present in data structured as graphs. In recent years, a burgeoning interest has arisen in exploiting the latent capabilities of GNN for healthcare-based applications, capitalizing on their aptitude for modeling complex relationships and unearthing profound insights from graph-structured data. However, to the best of our knowledge, no study has systemically reviewed the GNN studies conducted in the healthcare domain. This study has furnished an all-encompassing and erudite overview of the prevailing cutting-edge research on GNN in healthcare. Through analysis and assimilation of studies, current research trends, recurrent challenges, and promising future opportunities in GNN for healthcare applications have been identified. China emerged as the leading country to conduct GNN-based studies in the healthcare domain, followed by the USA, UK, and Turkey. Among various aspects of healthcare, disease prediction and drug discovery emerge as the most prominent areas of focus for GNN application, indicating the potential of GNN for advancing diagnostic and therapeutic approaches. This study proposed research questions regarding diverse aspects of GNN in the healthcare domain and addressed them through an in-depth analysis. This study can provide practitioners and researchers with profound insights into the current landscape of GNN applications in healthcare and can guide healthcare institutes, researchers, and governments by demonstrating the ways in which GNN can contribute to the development of effective and efficient healthcare systems.

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Section: Analysis and Interpretation Of Proposed Research Questionsmentioning

confidence: 99%

A Systematic Review of Graph Neural Network in Healthcare-Based Applications: Recent Advances, Trends, and Future Directions

Paul,

Saha,

Hasan

et al. 2024

IEEE Access

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Computational investigation of pyrazinamide drugs and its transition metal complexes using aDFTapproach

Khan,

Maha,

Riaz

et al. 2023

J Comput Chem

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Pyrazinamide, an antituberculosis but documented toxic drug, is subjected to computational investigation along with the metal complexes via a DFT approach to predict the structure–activity and structure‐toxicity relationship. 6‐31G(d,p) basis set was used for Zn, Ni, Mn, Fe, and Co, while the SDD basis set was applied to Cu, Cr, Cd, and Hg. Several reactivity parameters and charge distribution were calculated and the reactivity profile was estimated. The complexes were found to be soft and polarizable which could be responsible for their binding with bacterial targets to inhibit their growth. In contrast, pyrazinamide which is found to be hard among all is susceptible to being toxic. Moreover, the electronegative nature of the complexes can endow them with a better antibacterial effect. Since metal complexes have been found to be less toxic and more biologically interactive by computational methods, they can be employed as potent drugs for the cure of tuberculosis.

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Transformer technology in molecular science

Jiang,

Ke,

Chen

et al. 2024

WIREs Comput Mol Sci

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A transformer is the foundational architecture behind large language models designed to handle sequential data by using mechanisms of self‐attention to weigh the importance of different elements, enabling efficient processing and understanding of complex patterns. Recently, transformer‐based models have become some of the most popular and powerful deep learning (DL) algorithms in molecular science, owing to their distinctive architectural characteristics and proficiency in handling intricate data. These models leverage the capacity of transformer architectures to capture complex hierarchical dependencies within sequential data. As the applications of transformers in molecular science are very widespread, in this review, we only focus on the technical aspects of transformer technology in molecule domain. Specifically, we will provide an in‐depth investigation into the algorithms of transformer‐based machine learning techniques in molecular science. The models under consideration include generative pre‐trained transformer (GPT), bidirectional and auto‐regressive transformers (BART), bidirectional encoder representations from transformers (BERT), graph transformer, transformer‐XL, text‐to‐text transfer transformer, vision transformers (ViT), detection transformer (DETR), conformer, contrastive language‐image pre‐training (CLIP), sparse transformers, and mobile and efficient transformers. By examining the inner workings of these models, we aim to elucidate how their architectural innovations contribute to their effectiveness in processing complex molecular data. We will also discuss promising trends in transformer models within the context of molecular science, emphasizing their technical capabilities and potential for interdisciplinary research. This review seeks to provide a comprehensive understanding of the transformer‐based machine learning techniques that are driving advancements in molecular science.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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MolFPG: Multi-level fingerprint-based Graph Transformer for accurate and robust drug toxicity prediction

Cited by 13 publications

References 50 publications

A Systematic Review of Graph Neural Network in Healthcare-Based Applications: Recent Advances, Trends, and Future Directions

A Systematic Review of Graph Neural Network in Healthcare-Based Applications: Recent Advances, Trends, and Future Directions

Computational investigation of pyrazinamide drugs and its transition metal complexes using aDFTapproach

Transformer technology in molecular science

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