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 …

Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes

Y Yan, SY Huang - Briefings in bioinformatics, 2021 - academic.oup.com
Protein–protein interactions play a fundamental role in all cellular processes. Therefore,
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 …

Padme: A deep learning-based framework for drug-target interaction prediction

Q Feng, E Dueva, A Cherkasov, M Ester - arXiv preprint arXiv:1807.09741, 2018 - arxiv.org
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 …

Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks

Z Guo, J Liu, J Skolnick, J Cheng - Nature communications, 2022 - nature.com
Residue-residue distance information is useful for predicting tertiary structures of protein
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

MA Thafar, M Alshahrani, S Albaradei, T Gojobori… - Scientific reports, 2022 - nature.com
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 …

MCN-CPI: multiscale convolutional network for compound–protein interaction prediction

S Wang, M Jiang, S Zhang, X Wang, Q Yuan, Z Wei… - Biomolecules, 2021 - mdpi.com
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 …

Tapping on the black box: how is the scoring power of a machine-learning scoring function dependent on the training set?

M Su, G Feng, Z Liu, Y Li, R Wang - Journal of chemical …, 2020 - ACS Publications
In recent years, protein–ligand interaction scoring functions derived through machine-
learning are repeatedly reported to outperform conventional scoring functions. However …

Effective drug–target interaction prediction with mutual interaction neural network

F Li, Z Zhang, J Guan, S Zhou - Bioinformatics, 2022 - academic.oup.com
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 …

Persistent spectral based ensemble learning (PerSpect-EL) for protein–protein binding affinity prediction

JJ Wee, K Xia - Briefings in Bioinformatics, 2022 - academic.oup.com
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 …