{"title":"ImageDTA:药物与靶点结合亲和力预测的简单模型","authors":"Li Han, Ling Kang and Quan Guo*, ","doi":"10.1021/acsomega.4c02308","DOIUrl":null,"url":null,"abstract":"<p >Predicting the drug–target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this study, we propose a method based on a multiscale 2-dimensional convolutional neural network (CNN), namely ImageDTA. Many studies have shown that CNN achieves good learning effects with limited data. Therefore, we take a unique perspective by treating the word vector encoded with a simplified molecular input line entry system (SMILES) string as an “image” and processing it like handling images, fully leveraging the efficient processing capabilities of CNN for image data. Furthermore, we show that ImageDTA has higher training and inference efficiency than pretrained large models and outperforms attention-based graph neural network models in accuracy and interpretability. We also use visualization techniques to select appropriate convolutional kernel sizes, thereby increasing the network’s interpretability.</p>","PeriodicalId":22,"journal":{"name":"ACS Omega","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsomega.4c02308","citationCount":"0","resultStr":"{\"title\":\"ImageDTA: A Simple Model for Drug–Target Binding Affinity Prediction\",\"authors\":\"Li Han, Ling Kang and Quan Guo*, \",\"doi\":\"10.1021/acsomega.4c02308\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Predicting the drug–target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this study, we propose a method based on a multiscale 2-dimensional convolutional neural network (CNN), namely ImageDTA. Many studies have shown that CNN achieves good learning effects with limited data. Therefore, we take a unique perspective by treating the word vector encoded with a simplified molecular input line entry system (SMILES) string as an “image” and processing it like handling images, fully leveraging the efficient processing capabilities of CNN for image data. Furthermore, we show that ImageDTA has higher training and inference efficiency than pretrained large models and outperforms attention-based graph neural network models in accuracy and interpretability. We also use visualization techniques to select appropriate convolutional kernel sizes, thereby increasing the network’s interpretability.</p>\",\"PeriodicalId\":22,\"journal\":{\"name\":\"ACS Omega\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.acs.org/doi/epdf/10.1021/acsomega.4c02308\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Omega\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsomega.4c02308\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Omega","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsomega.4c02308","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
ImageDTA: A Simple Model for Drug–Target Binding Affinity Prediction
Predicting the drug–target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this study, we propose a method based on a multiscale 2-dimensional convolutional neural network (CNN), namely ImageDTA. Many studies have shown that CNN achieves good learning effects with limited data. Therefore, we take a unique perspective by treating the word vector encoded with a simplified molecular input line entry system (SMILES) string as an “image” and processing it like handling images, fully leveraging the efficient processing capabilities of CNN for image data. Furthermore, we show that ImageDTA has higher training and inference efficiency than pretrained large models and outperforms attention-based graph neural network models in accuracy and interpretability. We also use visualization techniques to select appropriate convolutional kernel sizes, thereby increasing the network’s interpretability.
ACS OmegaChemical Engineering-General Chemical Engineering
CiteScore
6.60
自引率
4.90%
发文量
3945
审稿时长
2.4 months
期刊介绍:
ACS Omega is an open-access global publication for scientific articles that describe new findings in chemistry and interfacing areas of science, without any perceived evaluation of immediate impact.