Results 31 to 40 of about 401,503 (258)

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities [PDF]

Frontiers in Chemistry, 2019
The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery.
Maha Thafar, Maha Thafar, Arwa Bin Raies, Somayah Albaradei, Somayah Albaradei, Magbubah Essack, Vladimir B. Bajic   +6 more
doaj   +4 more sources

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion [PDF]

Scientific Reports
Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug
Youzhi Liu, Linlin Xing, Longbo Zhang, Hongzhen Cai, Maozu Guo   +4 more
doaj   +2 more sources

GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information [PDF]

Biomolecules
Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has
Kusal Debnath, Pratip Rana, Preetam Ghosh   +2 more
doaj   +2 more sources

CASTER-DTA: equivariant graph neural networks for predicting drug-target affinity. [PDF]

Brief Bioinform
Abstract Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, scalable methods for predicting binding affinity
Kumar R, Romano JD, Ritchie MD.
europepmc   +4 more sources

MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction [PDF]

Frontiers in Genetics
Determining drug-target affinity (DTA) is a pivotal step in drug discovery, where in silico methods can significantly improve efficiency and reduce costs.
Xu Gao, Xu Gao, Mengfan Yan, Mengfan Yan, Chengwei Zhang, Chengwei Zhang, Gang Wu, Gang Wu, Jiandong Shang, Jiandong Shang, Congxiang Zhang, Congxiang Zhang, Kecheng Yang, Kecheng Yang   +13 more
doaj   +2 more sources

DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation [PDF]

Nature Communications
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery
Pir Masoom Shah, Huimin Zhu, Zhangli Lu, Kaili Wang, Jing Tang, Min Li   +5 more
doaj   +2 more sources

Drug–Target Affinity Prediction Based on Cross-Modal Fusion of Text and Graph

Applied Sciences
Drug–target affinity (DTA) prediction is a critical step in virtual screening and significantly accelerates drug development. However, existing deep learning-based methods relying on single-modal representations (e.g., text or graphs) struggle to fully ...
Jucheng Yang, Fushun Ren
doaj   +2 more sources

SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity

BMC Biology
Background Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research.
Xun Wang, Zhijun Xia, Runqiu Feng, Tongyu Han, Hanyu Wang, Wenqian Yu, Xingguang Wang   +6 more
doaj   +2 more sources

Studies and analysis of drug-target interactions by affinity chromatography and related techniques: A review

Journal of Pharmaceutical Analysis
The characterization of drug-target interactions is a key component of drug discovery, testing, and development. Affinity chromatography is one approach that can be used for this type of analysis.
David S. Hage, Sadia Sharmeen, B.K. Sajeeb, Harshana Olupathage, Md Masudur Rahman, Isaac Kyei, Samiul Alim, Nigar Sultana Pinky   +7 more
doaj   +3 more sources

Correction to: Breaking the barriers of data scarcity in drug-target affinity prediction. [PDF]

Brief Bioinform, 2023
europepmc   +2 more sources