[HTML][HTML] A review on compound-protein interaction prediction methods: data, format, representation and model
There has recently been a rapid progress in computational methods for determining protein
targets of small molecule drugs, which will be termed as compound protein interaction (CPI) …
targets of small molecule drugs, which will be termed as compound protein interaction (CPI) …
Explainable artificial intelligence: A taxonomy and guidelines for its application to drug discovery
I Ponzoni, JA Páez Prosper… - Wiley Interdisciplinary …, 2023 - Wiley Online Library
Artificial intelligence (AI) is having a growing impact in many areas related to drug discovery.
However, it is still critical for their adoption by the medicinal chemistry community to achieve …
However, it is still critical for their adoption by the medicinal chemistry community to achieve …
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
Predicting drug–target affinity (DTA) is beneficial for accelerating drug discovery. Graph
neural networks (GNNs) have been widely used in DTA prediction. However, existing …
neural networks (GNNs) have been widely used in DTA prediction. However, existing …
ML-DTI: mutual learning mechanism for interpretable drug–target interaction prediction
Deep learning (DL) provides opportunities for the identification of drug–target interactions
(DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most …
(DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most …
Modality-DTA: multimodality fusion strategy for drug–target affinity prediction
Prediction of the drug–target affinity (DTA) plays an important role in drug discovery. Existing
deep learning methods for DTA prediction typically leverage a single modality, namely …
deep learning methods for DTA prediction typically leverage a single modality, namely …
[HTML][HTML] A brief review of protein–ligand interaction prediction
The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …
Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction
Y Zhang, S Li, K Meng, S Sun - Journal of Chemical Information …, 2024 - ACS Publications
Developing new drugs is too expensive and time-consuming. Accurately predicting the
interaction between drugs and targets will likely change how the drug is discovered …
interaction between drugs and targets will likely change how the drug is discovered …
Improving evidential deep learning via multi-task learning
Abstract The Evidential regression network (ENet) estimates a continuous target and its
predictive uncertainty without costly Bayesian model averaging. However, it is possible that …
predictive uncertainty without costly Bayesian model averaging. However, it is possible that …
Drug–target interaction prediction based on protein features, using wrapper feature selection
Drug–target interaction prediction is a vital stage in drug development, involving lots of
methods. Experimental methods that identify these relationships on the basis of clinical …
methods. Experimental methods that identify these relationships on the basis of clinical …
DeepStack-DTIs: Predicting drug–target interactions using LightGBM feature selection and deep-stacked ensemble classifier
Y Zhang, Z Jiang, C Chen, Q Wei, H Gu… - Interdisciplinary Sciences …, 2022 - Springer
Accurate prediction of drug–target interactions (DTIs), which is often used in the fields of
drug discovery and drug repositioning, is regarded a key challenge in the study of drug …
drug discovery and drug repositioning, is regarded a key challenge in the study of drug …