A review of deep learning methods for ligand based drug virtual screening
Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process.
Hongjie Wu +6 more
doaj +1 more source
An epithelial GPR35 isoform supports tumor‐associated transcriptional and metabolic phenotypes
GPR35 generates two functionally distinct isoforms with previously unresolved roles. GPR35‐short mediates immune‐cell chemotaxis, while GPR35‐long is enriched in colorectal cancer epithelium, where it supports increased metabolism, proliferation, and tumor‐associated transcriptional programs.
Jørgen D. Rønneberg +14 more
wiley +1 more source
Structure‐forward targeting of claudins with synthetic binders
Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
wiley +1 more source
Drug-target affinity prediction method based on consistent expression of heterogeneous data
The first step in drug discovery is finding drug molecule moieties with medicinal activity against specific targets. Therefore, it is crucial to investigate the interaction between drug-target proteins and small chemical molecules.
Liu, Boyuan
core
Engineering peptides into antibodies—opportunities and strategies for therapeutic innovation
Peptides and antibodies occupy complementary therapeutic niches. Peptides recognize difficult targets in a compact format, while antibodies add specificity, long half‐life, and effector functions. This review examines strategies that merge both modalities—peptide grafting into loops, terminal and Fc fusions, and bioconjugation—highlighting how ...
Jinling Wang +2 more
wiley +1 more source
Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction
Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency ...
Xianfang Wang +6 more
doaj +1 more source
Synergistic perspectives—How single‐molecule biophysics complement biochemical understanding
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 +2 more
wiley +1 more source
ResDTA: Predicting Drug-Target Binding Affinity Using Residual Skip Connections
The discovery of novel drug target (DT) interactions is an important step in the drug development process. The majority of computer techniques for predicting DT interactions have focused on binary classification, with the goal of determining whether or ...
Ghosh, Partho, Haque, Md. Aynal
core
Structural and biochemical analysis of a B12 superbinder
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 +3 more
wiley +1 more source
Bispecific antibodies: A guide to model informed drug discovery and development
Affinity (KD) optimization of monoclonal antibodies is one of the factors that impacts the stoichiometric binding and the corresponding efficacy of a drug.
Irina Kareva +3 more
doaj +1 more source

