BMC Pharmacology and Toxicology

Journal of the American Medical Informatics Association

Bioinformatics

Drug Safety

JOURNAL OF MEDICAL INTERNET RESEARCH

arXiv

Journal of Cheminformations

Journal of Chemical Information and Modeling

Journal of Computer Aided Molecular Design

2020/03/06

Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions [paper]

2019

A Model to Search for Synthesizable Molecules [paper] [code]

Machine learning for molecular simulation [paper]

2017

BMC Pharmacology and Toxicology_Data-driven prediction of adverse drug reactions induced by drug-drug interactions

JIMIA_Deep learning for pharmacovigilance recurrent neural network architectures for labeling adverse drug reactions in Twitter posts

2018

Bioinformatics_Modeling polypharmacy side effects with graph convolutional networks

2019

Drug safety_Adverse drug event detection from electronic health records using hierarchical recurrent neural networks with dual-level embedding

Drug safety_Adverse drug events detection in clinical notes by jointly modeling entities and relations using neural networks

Drug safety_Detecting adverse drug events with rapidly trained classification models

JIMAI_Comment on:"Deep learning for pharmacovigilance- recurrent neural network architectures for labeling adverse drug reactions in Twitter posts"

Journal of medical Internet research_Detecting potential adverse drug reactions using a deep neural network model

2015

arXiv_AtomNet- a deep convolutional neural network for bioactivity prediction in structure-based drug discovery

2017

Journal of computational chemistry_The role of different sampling methods in improving biological activity prediction using deep belief network

2018

2018_ICPR_Graph memory networks for molecular activity prediction

2018

Molecular pharmaceutics_Comparing and validating machine learning models for Mycobacterium tuberculosis drug discovery

Molecular pharmaceutics_Prototype-based compound discovery using deep generative models

2019

Journal of cheminformatics_Multi-channel PINN- investigating scalable and transferable neural networks for drug discovery

Drug Bio-Target Interaction

2010

Journal of chemical information and modeling_NNScore- a neural-network-based scoring function for the characterization of protein−ligand complexes

Drug Chem-Target Interaction

2011

Journal of chemical information and modeling_NNScore 2.0- a neural-network receptor¨Cligand scoring function

2013

2013_Bioinformatics_Predicting drug-target interactions using restricted Boltzmann machines

2014

2014_IEEE BIBM_Pairwise input neural network for target-ligand interaction prediction

2015

BMC bioinformatics_BgN-Score and BsN-Score- bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes

2016

Methods_Boosting compound-protein interaction prediction by deep learning

bioRxiv_Deep learning with feature embedding for compound-protein interaction prediction

2017

Bioinformatics_Deep mining heterogeneous networks of biomedical linked data to predict novel drug¨Ctarget associations

IEEE BIBM_Drug¡ªtarget interaction prediction with a deep-learning-based model

Journal of chemical information and modeling_Protein¨Cligand scoring with convolutional neural networks

Journal of proteome research_Deep-learning-based drug¨Ctarget interaction prediction

arXiv_Atomic convolutional networks for predicting protein-ligand binding affinity

2018

BMC_genomics_Deep learning-based transcriptome data classification for drug-target interaction prediction

Bioinformatics_Compound¨Cprotein interaction prediction with end-to-end learning of neural networks for graphs and sequences

Bioinformatics_DeepDTA deep drug¨Ctarget binding affinity prediction

Bioinformatics_Development and evaluation of a deep learning model for protein¨Cligand binding affinity prediction

Current opinion in structural biology_Statistical and machine learning approaches to predicting protein¨Cligand interactions

Journal of chemical information and modeling_DEEP- protein¨Cligand absolute binding affinity prediction via 3D-convolutional neural networks

2019

BioRxiv_GraphDTA prediction of drug¨Ctarget binding affinity using graph convolutional networks

PLoS computational biology_DeepConv-DTI Prediction of drug-target interactions via deep learning with convolution on protein sequences

1997

Journal of chemical information and computer sciences_A neural device for searching direct correlations between structures and properties of chemical compounds

2017

Journal of chemical information and modeling_Convolutional embedding of attributed molecular graphs for physical property prediction

arXiv_Smiles2vec- An interpretable general-purpose deep neural network for predicting chemical properties

2018

Scientific reports_Prediction of pKa values for neutral and basic drugs based on hybrid artificial intelligence methods

2019

International journal of molecular sciences_Chemi-Net- a molecular graph convolutional network for accurate drug property prediction

2005

Current Computer-Aided Drug Design_Kohonen artificial neural network and counter propagation neural network in molecular structure-toxicity studies

2015

Journal of chemical information and modeling_Deep learning for drug-induced liver injury

arXiv_Toxicity prediction using deep learning

2016

Frontiers in Environmental Science_DeepTox toxicity prediction using deep learning

2017

Chemical research in toxicology_Deep learning to predict the formation of quinone species in drug metabolism

Journal of chemical information and modeling_Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction

2018

Journal of chemical information and modeling_Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space

2019

Journal of chemical information and modeling_Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity

2016

arXiv_Modeling industrial ADMET data with multitask networks

2017

Journal of cheminformatics_Beyond the hype- deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

2019

Journal of chemical information and modeling_Predictive Multitask Deep Neural Network Models for ADME-Tox Properties- Learning from Large Data Sets

2016

Journal of computer-aided molecular design_Molecular graph convolutions moving beyond fingerprints

2017

Journal of cheminformatics_Molecular de-novo design through deep reinforcement learning

2018

Journal of chemical information and modeling_De Novo Molecule Design by Translating from Reduced Graphs to SMILES

Journal of chemical information and modeling_Reinforced adversarial neural computer for de novo molecular design

Journal of cheminformatics_Multi-objective de novo drug design with conditional graph generative model

Molecular informatics_Application of generative autoencoder in de novo molecular design

Molecular informatics_De novo design of bioactive small molecules by artificial intelligence

Molecular informatics_Generative recurrent networks for de novo drug design

Molecular pharmaceutics_Adversarial threshold neural computer for molecular de novo design

Molecular pharmaceutics_Entangled conditional adversarial autoencoder for de novo drug discovery

Science advances_Deep reinforcement learning for de novo drug design

2019

Journal of chemical information and modeling_De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping

Journal of chemical information and modeling_Guacamol- benchmarking models for de novo molecular design

Journal of cheminformatics_An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning- a case for the adenosine A 2A receptor

Shape-Based Generative Modeling for de Novo Drug Design

2013

Journal of chemical information and modeling_Deep architectures and deep learning in chemoinformatics the prediction of aqueous solubility for drug-like molecules

Journal of chemical information and modeling_Deep architectures and deep learning in chemoinformatics- the prediction of aqueous solubility for drug-like molecules

2015

ACS central science_Modeling epoxidation of drug-like molecules with a deep machine learning network

1996

Journal of pharmaceutical sciences_Artificial neural networks as a novel approach to integrated pharmacokinetic¡ªpharmacodynamic analysis

2006

Journal of chemical information and modeling_Applications of self-organizing neural networks in virtual screening and diversity selection

2011

European Journal of Medicinal Chemistry_Ligand-based virtual screening procedure for the prediction and the identification of novel ¦Â-amyloid aggregation inhibitors

2014

Proceedings of the deep learning workshop at NIPS_Deep learning as an opportunity in virtual screening

2016

Journal of chemical information and modeling_Boosting docking-based virtual screening with deep learning

arXiv_Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening

2018

Computers in biology and medicine_Interaction prediction in structure-based virtual screening using deep learning

2019

Journal of chemical information and modeling_In need of bias control- Evaluating chemical data for machine learning in structure-based virtual screening

[1] Prasad, Vinay, and Sham Mailankody. “Research and development spending to bring a single cancer drug to market and revenues after approval.” JAMA internal medicine 177.11 (2017): 1569-1575.

[2] Lavecchia, Antonio. “Machine-learning approaches in drug discovery: methods and applications.” Drug discovery today 20.3 (2015): 318-331.

[3] Burbidge, Robert, et al. “Drug design by machine learning: support vector machines for pharmaceutical data analysis.” Computers & chemistry 26.1 (2001): 5-14.

[4] Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 2010, 26:1169–1175.

[5] Mitchell, John BO. “Machine learning methods in chemoinformatics.” Wiley Interdisciplinary Reviews: Computational Molecular Science 4.5 (2014): 468-481.

[6] Ekins, Sean. “The next era: deep learning in pharmaceutical research.” Pharmaceutical research 33.11 (2016): 2594-2603.

[7] Liu, Ke, et al. “Chemi-Net: a molecular graph convolutional network for accurate drug property prediction.” International journal of molecular sciences 20.14 (2019): 3389.