Results 101 to 110 of about 8,194,451 (253)

Network-Based Methods for Prediction of Drug-Target Interactions

Frontiers in Pharmacology, 2018
Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and ...
Zengrui Wu, Weihua Li, Guixia Liu, Yun Tang   +3 more
doaj   +1 more source

Synergistic perspectives—How single‐molecule biophysics complement biochemical understanding

FEBS Letters, EarlyView.
In this review, we discuss how ensemble biochemistry and single‐molecule approaches are complementary, outline commonly used single‐molecule techniques, and illustrate their relevance through two representative case studies: chromatin organization by SMC complexes and pathway choice during DNA double‐strand break repair.
Sara De Bragança, Eline L. Jansen, Jorine M. Eeftens   +2 more
wiley   +1 more source

Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces

BMC Bioinformatics, 2019
Background Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery.
Yi Zheng, Hui Peng, Xiaocai Zhang, Zhixun Zhao, Xiaoying Gao, Jinyan Li   +5 more
doaj   +1 more source

Translophagy—A potential link between autophagy impairment and translational errors

FEBS Letters, EarlyView.
Neurodegenerative diseases are characterised by the accumulation of abnormal proteins and protein aggregates, but their origin often remains unknown. We propose that selective autophagy removes damaged protein‐making machinery, preventing errors during protein synthesis.
Mykola V. Korolchuk, Gailing Ma, Robin Antrobus, Diego Manni, Marco Trevisan‐Herraz, Mustafa Khwaja, Max Brown, Gavin D. Richardson, Masaaki Komatsu, Daniel Erskine, Viktor I. Korolchuk, Tetsushi Kataura   +11 more
wiley   +1 more source

Biases of drug-target interaction network data

, 2020
. Network based prediction of interaction between drug compounds and target proteins is a core step in the drug discovery process. The availability of drug-target interaction data has boosted the development of machine learning methods for the in silico ...
Twan Van Laarhoven, Elena Marchiori
core  

Drug target identification in protozoan parasites.

, 2016
INTRODUCTION Despite the fact that diseases caused by protozoan parasites represent serious challenges for public health, animal production and welfare, only a limited panel of drugs has been marketed for clinical applications.
Joachim Müller, Andrew Hemphill, Müller, Joachim, Hemphill, Andrew   +3 more
core   +1 more source

GCARDTI: Drug–target interaction prediction based on a hybrid mechanism in drug SELFIES

Quantitative Biology
The prediction of the interaction between a drug and a target is the most critical issue in the fields of drug development and repurposing. However, there are still two challenges in current deep learning research: (i) the structural information of drug ...
Yinfei Feng, Yuanyuan Zhang, Zengqian Deng, Mimi Xiong   +3 more
doaj   +1 more source

Drug Target Interaction Prediction Using Machine Learning Techniques – A Review.

International Journal of Interactive Multimedia and Artificial Intelligence
Drug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one’s life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host ...
A. Suruliandi, T. Idhaya, S. P. Raja
doaj   +1 more source

Structural and biochemical analysis of a B12 superbinder

FEBS Letters, EarlyView.
BtuG proteins are vitamin B12 scavengers in Bacteroides thetaiotaomicron, a dominant human gut bacterium. We present crystal structures of three BtuG homologs bound to cobalamin and its precursor cobinamide, revealing picomolar binding affinities, among the highest known for any natural protein.
Jose M. Martinez Felices, Jacob J. Whittaker, Dirk J. Slotboom, Albert Guskov   +3 more
wiley   +1 more source

ISAAC: Prior-aligned structural sensitivity auditing for drug–target interaction models

Artificial Intelligence in the Life Sciences
Deep learning models for drug–target interaction (DTI) prediction often achieve strong benchmark performance while relying on input patterns that are not captured by standard accuracy-based evaluation.
Barbara Tarantino, Sun Kim, Yijingxiu Lu, Paolo Giudici   +3 more
doaj   +1 more source