# Papers de *binding affinity ligand-protein*

### Titulo del paper + link en github

#### Paper Nature (2021)

* (2021) [Prediction of pharmacological activities from chemical structures with graph convolutional neural networks](https://www.nature.com/articles/s41598-020-80113-7)

#### MPN & GCN

* (2020) [Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0407-y)

Código GitHub: https://github.com/edvardlindelof/graph-neural-networks-for-drug-discovery

#### OnionNet
* (2019) [OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein−Ligand Binding Affinity Prediction](https://pubs.acs.org/doi/10.1021/acsomega.9b01997)

Código GitHub: https://github.com/zhenglz/onionnet

Para crear los features: https://github.com/zhenglz/onionnet_featurize


#### GraphDTA
* (2020) [GraphDTA: Predicting drug–target binding affinity with graph neural networks](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btaa921/5942970?redirectedFrom=fulltext)

Código GitHub: https://github.com/thinng/GraphDTA

Post-hoc analysis: https://zenodo.org/record/3603523


#### DEEPScreen

* (2020) [DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations](https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc03414e#!divAbstract)

Código GitHub: https://github.com/cansyl/DEEPscreen


#### Paper de Nature
* (2020)[Predicting drug–protein interaction using quasi-visual question answering system](https://www.nature.com/articles/s42256-020-0152-y#change-history)

Código GitHub: https://github.com/masashitsubaki/CPI_prediction


#### Pafnucy

* (2018) [Development and evaluation of a deep learning model for protein–ligand binding affinity prediction](https://academic.oup.com/bioinformatics/article/34/21/3666/4994792)

* Código GitLab: http://gitlab.com/cheminfIBB/pafnucy


#### DeepDTA

* (2018) [DeepDTA: deep drug–target binding affinity prediction](https://academic.oup.com/bioinformatics/article/34/17/i821/5093245)

Código github: https://github.com/hkmztrk/DeepDTA/


#### DeepBindRG

* (2019) [DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity](https://peerj.com/articles/7362/)

Código GitHub: https://github.com/haiping1010/DeepBindRG


#### SAMPN

* (2020) [A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-0414-z#Tab1)

Código: https://github.com/tbwxmu/SAMPN


* (2017) [SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0209-z)
Acá está la descripción de KIBA y Davis dataset

Código y datasets: https://zenodo.org/record/164436

## Arxiv Papers

* (2019) [PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction](https://arxiv.org/abs/1807.09741)

* (2020) [Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference](https://arxiv.org/abs/2005.07704)

Código: https://github.com/llnl/fast

* (2018) [Visualizing Convolutional Neural Network Protein-Ligand Scoring](https://arxiv.org/abs/1803.02398)

* (2019) [WideDTA: prediction of drug-target binding affinity](https://arxiv.org/abs/1902.04166)

* (2018) [ChemBoost: A chemical language based approach for protein-ligand binding affinity prediction](https://arxiv.org/abs/1811.00761)

* (2019) [DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction](https://arxiv.org/abs/1912.00318)

* (2020) [InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation](https://arxiv.org/abs/2005.13438)

* (2019) [PIGNet: A physics-informed deep learning model toward generalized drug-target interaction predictions](https://arxiv.org/abs/2008.12249)

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**Otros papers sin código** (útil para referencias o introducción, discusión, etc)

* (2019) [Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction ](https://static1.squarespace.com/static/59d5ac1780bd5ef9c396eda6/t/5d472f63eebdc3000174efea/1564946292553/Shin.pdf)

Código demo: https://mt-dti.deargendev.me/intro

* (2020) [Interpretable deep learning framework for binding affinity prediction](https://dspace.mit.edu/handle/1721.1/127527)

* (2020) [Rapid, accurate, precise and reproducible ligand–protein binding free energy prediction](https://royalsocietypublishing.org/doi/full/10.1098/rsfs.2020.0007)

* (2020) [AttentionDTA: prediction of drug–target binding affinity using attention model](https://ieeexplore.ieee.org/document/8983125)

* (2018)[KDEEP: Protein−Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.7b00650?casa_token=szRnn6x_7hIAAAAA:DAO1J5fA9eYThoB7ccU2r0Dy8Pz4RWXtf0zf9ByP4fCcyFvSfw1a-WuhFuPnWQnxNr6htS3hYX34sqHT)

* (2020) [Learning from the ligand: using ligand-based features to improve binding affinity prediction](https://academic.oup.com/bioinformatics/article-abstract/36/3/758/5554651)

Resources: http://opig.stats.ox.ac.uk/resources

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## Embeddings de moléculas

## C-SGEN
* (2019) [Molecule Property Prediction Based on Spatial Graph Embedding](https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00410?rand=kwdp99v2)


Código GitHub: https://github.com/wxfsd/C-SGEN

## Conv-qsar-fast

* (2017) [Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction](https://core.ac.uk/download/pdf/159548591.pdf)

[Link en JCIM](https://pubs.acs.org/doi/10.1021/acs.jcim.6b00601)


Código GitHub: https://github.com/connorcoley/conv_qsar_fast