DeepDTA_Deep Drug-Target Binding Affinity Prediction2018.pdf

所需积分/C币:46 2020-03-20 14:57:17 388KB PDF
180
收藏 收藏
举报

DTI预测领域的高被引lw,发表于2018年. Abstract Motivation The identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein–ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein–ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).

...展开详情
试读 18P DeepDTA_Deep Drug-Target Binding Affinity Prediction2018.pdf
立即下载
限时抽奖 低至0.43元/次
身份认证后 购VIP低至7折
一个资源只可评论一次,评论内容不能少于5个字
您会向同学/朋友/同事推荐我们的CSDN下载吗?
谢谢参与!您的真实评价是我们改进的动力~
关注 私信
上传资源赚钱or赚积分
最新推荐
DeepDTA_Deep Drug-Target Binding Affinity Prediction2018.pdf 46积分/C币 立即下载
1/18
DeepDTA_Deep Drug-Target Binding Affinity Prediction2018.pdf第1页
DeepDTA_Deep Drug-Target Binding Affinity Prediction2018.pdf第2页
DeepDTA_Deep Drug-Target Binding Affinity Prediction2018.pdf第3页
DeepDTA_Deep Drug-Target Binding Affinity Prediction2018.pdf第4页

试读结束, 可继续读2页

46积分/C币 立即下载