2020年7月1日 · In this paper, DTA has been achieved using three different approaches: (i) by establishing PDUE conditions in subregion-level, (ii) by establishing DSO conditions in …

• Explain the basic concepts of DTA and various DTA definitions and implementations, • Highlight the types of transportation analysis applications for which DTA models could be found useful, • …

2025年1月28日 · This study proposes DMFF-DTA, a dual-modality neural network model integrates sequence and graph structure information from drugs and proteins for drug-target …

2025年1月20日 · We propose DHAG-DTA, a general dynamic hierarchical affinity graph DNN approach, for DTA prediction using molecular sequence information and already known drug …

This guidebook explains how to approach the development and application of a transportation model with DTA for alternatives analysis. Dynamic Traffic Assignment is an evolving technique …

2025年1月7日 · To overcome these challenges, we propose a multimodal, multiscale model based on Sequence and Graph Modalities for Drug-Target Affinity (MMSG-DTA) Prediction. …

2025年1月20日 · In this paper, we propose a novel multimodal deep learning model named MMPD-DTA for predicting drug-target binding affinity to address these challenges. The MMPD …

2020年10月24日 · We propose a novel deep learning model called GraphDTA for drug–target affinity (DTA) prediction. We frame the DTA prediction problem as a regression task where the …

2023年1月27日 · We propose a novel Multi-Functional and Robust Drug–Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new …

DynusT is the most widely used simulation-based Dynamic Traffic Assignment (DTA) model with more than 200 projects/publications by users worldwide due to its superior traffic simulation …