Awesome_AI_Aided_Drug_Design_Papers

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Awesome_AI_Aided_Drug_Design_Papers

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https://chenxingqiang.github.io/Awesome_AI_Aided_Drug_Design_Papers/ focusing on ai, gnn, drug, Virtual Screening, Journals.

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# **Awesome AI Aided Drug Design Papers**

## Journals

**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**

## **arXiv hot Papers with code**

*2020/03/06*

Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature  Interactions <a href="https://arxiv.org/abs/2003.02587">[paper] </a>

*2019*

A Model to Search for Synthesizable Molecules <a href="https://arxiv.org/abs/1906.05221">[paper]</a>  <a href="https://github.com/john-bradshaw/molecule-chef"> [code] </a>

Machine learning for molecular simulation <a href="https://arxiv.org/pdf/1911.02792.pdf"> [paper] </a>

## ADR  
*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
                     
## Activity Prediction

*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

## Drug Discovery  
*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–Target Interaction

*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
 
## Property Prediction

*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
    
## Toxic

*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

## AMDET

*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
  
                  
## De Novo Drug Design

*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
   
    
## Drug-Like 
*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
                
## PK-PD

*1996*

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

*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

## To be placed on files

[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.

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