GSAML-DTA: An interpretable drug-target binding affinity prediction model based on graph neural networks with self-attention mechanism and mutual information

PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods

(2022) Weiqi Xia et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Deep drug-target binding affinity prediction with multiple attention blocks

(2021) Yuni Zeng et al.   BRIEFINGS IN BIOINFORMATICS

Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors

(2021) Arpan Mukherjee et al.   Journal of Chemical Information and Modeling

Mining Toxicity Information from Large Amounts of Toxicity Data

(2021) Zhenxing Wu et al.   JOURNAL OF MEDICINAL CHEMISTRY

Identifying drug–target interactions based on graph convolutional network and deep neural network

(2020) Tianyi Zhao et al.   BRIEFINGS IN BIOINFORMATICS

Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019

(2020) David J. Newman et al.   JOURNAL OF NATURAL PRODUCTS

An open source chemical structure curation pipeline using RDKit

(2020) A. Patrícia Bento et al.   Journal of Cheminformatics

Explainable Deep Relational Networks for Predicting Compound–Protein Affinities and Contacts

(2020) Mostafa Karimi et al.   Journal of Chemical Information and Modeling

HH-suite3 for fast remote homology detection and deep protein annotation

(2019) Martin Steinegger et al.   BMC BIOINFORMATICS

Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery

(2019) Jiajun Hong et al.   BRIEFINGS IN BIOINFORMATICS

Graph Convolutional Neural Networks for Predicting Drug-Target Interactions

(2019) Wen Torng et al.   Journal of Chemical Information and Modeling

Compound-protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences

(2018) Masashi Tsubaki et al.   BIOINFORMATICS

Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder

(2018) Weiwei Xue et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines

(2017) Tong He et al.   Journal of Cheminformatics

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

(2017) Eelke B. Lenselink et al.   Journal of Cheminformatics

Toward more realistic drug-target interaction predictions

(2014) T. Pahikkala et al.   BRIEFINGS IN BIOINFORMATICS

Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis

(2014) Jing Tang et al.   Journal of Chemical Information and Modeling

Some case studies on application of “rm2” metrics for judging quality of quantitative structure-activity relationship predictions: Emphasis on scaling of response data

(2013) Kunal Roy et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Stress Effects on FosB- and Interleukin-8 (IL8)-driven Ovarian Cancer Growth and Metastasis

(2010) Mian M. K. Shahzad et al.   JOURNAL OF BIOLOGICAL CHEMISTRY

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

(2009) Garrett M. Morris et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY