Yuel: Improving the generalizability of structure-free compound–protein interaction prediction
J Wang, NV Dokholyan - Journal of chemical information and …, 2022 - ACS Publications
Predicting binding affinities between small molecules and the protein target is at the core of
computational drug screening and drug target identification. Deep learning-based …
computational drug screening and drug target identification. Deep learning-based …
Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes
Protein–protein interactions play a fundamental role in all cellular processes. Therefore,
determining the structure of protein–protein complexes is crucial to understand their …
determining the structure of protein–protein complexes is crucial to understand their …
Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power
Z Wang, H Sun, X Yao, D Li, L Xu, Y Li… - Physical Chemistry …, 2016 - pubs.rsc.org
As one of the most popular computational approaches in modern structure-based drug
design, molecular docking can be used not only to identify the correct conformation of a …
design, molecular docking can be used not only to identify the correct conformation of a …
Padme: A deep learning-based framework for drug-target interaction prediction
In silico drug-target interaction (DTI) prediction is an important and challenging problem in
biomedical research with a huge potential benefit to the pharmaceutical industry and …
biomedical research with a huge potential benefit to the pharmaceutical industry and …
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
Residue-residue distance information is useful for predicting tertiary structures of protein
monomers or quaternary structures of protein complexes. Many deep learning methods …
monomers or quaternary structures of protein complexes. Many deep learning methods …
Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
drug screening. Most DTI prediction methods cast the problem as a binary classification task …
MCN-CPI: multiscale convolutional network for compound–protein interaction prediction
In the process of drug discovery, identifying the interaction between the protein and the
novel compound plays an important role. With the development of technology, deep learning …
novel compound plays an important role. With the development of technology, deep learning …
Tapping on the black box: how is the scoring power of a machine-learning scoring function dependent on the training set?
In recent years, protein–ligand interaction scoring functions derived through machine-
learning are repeatedly reported to outperform conventional scoring functions. However …
learning are repeatedly reported to outperform conventional scoring functions. However …
Effective drug–target interaction prediction with mutual interaction neural network
Motivation Accurately predicting drug–target interaction (DTI) is a crucial step to drug
discovery. Recently, deep learning techniques have been widely used for DTI prediction and …
discovery. Recently, deep learning techniques have been widely used for DTI prediction and …
Persistent spectral based ensemble learning (PerSpect-EL) for protein–protein binding affinity prediction
Protein–protein interactions (PPIs) play a significant role in nearly all cellular and biological
activities. Data-driven machine learning models have demonstrated great power in PPIs …
activities. Data-driven machine learning models have demonstrated great power in PPIs …