└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # A List of papers Deep Learning in Chemistry 2 | 3 | This is a list of implementations of deep learning methods to chemistry. A list of deep learing in biology can be found in Mikael Huss' [repo](https://github.com/hussius/deeplearning-biology). 4 | 5 | ## 1. Reviews of deep learning in chemistry 6 | ### General Introduction 7 | 1. [Deep Learning in Chemistry](https://pubs.acs.org/doi/10.1021/acs.jcim.9b00266) (2019) 8 | 2. [Deep Learning for Computational Chemistry](https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcc.24764) (2017) 9 | 3. The convergence of artificial intelligence and chemistry for improved drug discovery. [Future Science](https://www.future-science.com/doi/full/10.4155/fmc-2018-0161) 10 | 4. A Survey of Multi‐task Learning Methods in Chemoinformatics. [Molecular Informatics](https://onlinelibrary.wiley.com/doi/full/10.1002/minf.201800108) 11 | 5. Deep learning: new computational modelling techniques for genomics. [Nature Reviews Genetics](https://www.nature.com/articles/s41576-019-0122-6) 12 | 6. Deep Learning in Biomedical Data Science. [Annual Review of Biomedical Data Science](https://www.annualreviews.org/doi/abs/10.1146/annurev-biodatasci-080917-013343) 13 | 7. Deep learning for computational biology. [Mol Syst Biol ](https://www.embopress.org/doi/full/10.15252/msb.20156651) 14 | ### Predicting Properities 15 | 1. [MoleculeNet: A Benchmark for Molecular Machine Learning](https://arxiv.org/abs/1703.00564) (2017) 16 | ### Molecular (drug and materials) Design 17 | 1. [The rise of deep learning in drug discovery](https://www.sciencedirect.com/science/article/pii/S1359644617303598) (2018) 18 | 2. [The Next Era: Deep Learning in Pharmaceutical Research](https://www.ncbi.nlm.nih.gov/pubmed/27599991) (2016) 19 | 3. [Deep learning for molecular design - a review of the state of the art](https://arxiv.org/abs/1903.04388) (2019) 20 | 4. Rethinking drug design in the artificial intelligence era. [Nature Review](https://www.nature.com/articles/s41573-019-0050-3) 21 | ### Synthesis Planing 22 | 1. [Machine Learning in Computer-Aided Synthesis Planning](https://pubs.acs.org/doi/10.1021/acs.accounts.8b00087) (2018) 23 | ### Transfer learning and multitask learning 24 | 1. [Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5807924/) (2018) 25 | ### MD Simulation 26 | Shasha Feng has built a [repo](https://github.com/sha256feng/mldl-md-dynamics) of machine learning/deep learning in molecular dynamics. 27 | 1. [Machine learning approaches for analyzing and enhancing molecular dynamics simulations](https://arxiv.org/abs/1909.11748) (2019) 28 | 29 | 30 | ## 2. Applications by Molecular Representations 31 | 32 | ### 2.1. Molecular Graph 33 | 34 | Mufei Li has complied a list of deep learning for molecular graph: [DL4MolecularGraph](https://github.com/mufeili/DL4MolecularGraph). 35 | 36 | 1. Sparse hierarchical representation learning on molecular graphs. [arXiv](https://arxiv.org/abs/1908.02065) 37 | 2. Deep Generative Models for 3D Compound Design. [BioRxiv](https://www.biorxiv.org/content/10.1101/830497v1) 38 | 39 | ### 2.2. SMILES and Analogues 40 | 41 | #### Data Mining 42 | 1. Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature. [J. Chem. Inf. Model. 2019.](https://pubs.acs.org/doi/10.1021/acs.jcim.9b00470) | [ChemRxiv](https://chemrxiv.org/articles/Named_Entity_Recognition_and_Normalization_Applied_to_Large-Scale_Information_Extraction_from_the_Materials_Science_Literature/8226068/1) | [Github](https://github.com/materialsintelligence/matscholar) 43 | 2. Molecular Structure Extraction from Documents Using Deep Learning. [J. Chem. Inf. Model.](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.8b00669) 44 | 3. BioSentVec: creating sentence embeddings for biomedical texts. [IEEE](https://ieeexplore.ieee.org/abstract/document/8904728) 45 | 46 | #### Synthesis Planing 47 | 1. Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction. [ACS Cent. Sci.2019](https://pubs.acs.org/doi/10.1021/acscentsci.9b00576) | [ChemRxiv](https://chemrxiv.org/articles/Molecular_Transformer_for_Chemical_Reaction_Prediction_and_Uncertainty_Estimation/7297379) | [Github](https://github.com/pschwllr/MolecularTransformer) 48 | 2. Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space. [Chem. Commun. 2019](https://pubs.rsc.org/en/content/articlelanding/2019/CC/c9cc05122h#!divAbstract) 49 | 50 | #### Molecular Design 51 | 1. Re-balancing Variational Autoencoder Loss for Molecule Sequence Generation. (2019) [arXiv](https://arxiv.org/abs/1910.00698) 52 | 2. Generating customized compound libraries for drug discovery with machine intelligence (2019) [ChemRxiv](https://chemrxiv.org/articles/Generating_Customized_Compound_Libraries_for_Drug_Discovery_with_Machine_Intelligence/10119299) [Github](https://github.com/ETHmodlab/virtual_libraries) 53 | 3. Deep Generative Models for 3D Compound Design. (2019) [BioRxiv](https://www.biorxiv.org/content/10.1101/830497v1) [Github](https://github.com/oxpig/DeLinker) 54 | 4. Molecular Generative Model Based On Adversarially Regularized Autoencoder. [arXiv](https://arxiv.org/abs/1912.05617). [Github](https://github.com/gicsaw/ARAE_SMILES) 55 | 56 | #### Image 57 | 1. Learning Drug Function from Chemical Structure with Convolutional Neural Networks and Random Forests. [BioRxiv](https://www.biorxiv.org/content/10.1101/482877v2.full) 58 | 59 | #### 3D Structure 60 | 1. Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis. [Front. Bioeng. Biotechnol.](https://www.frontiersin.org/articles/10.3389/fbioe.2019.00065/full) 61 | 62 | ## 4. Transfer Learning 63 | A list of transfer learning in chemistry. 64 | 1. Inductive Transfer Learning for Molecular Activity Prediction: Next-Gen QSAR Models with MolPMoFiT. [ChemRxiv](https://chemrxiv.org/articles/Inductive_Transfer_Learning_for_Molecular_Activity_Prediction_Next-Gen_QSAR_Models_with_MolPMoFiT/9978743/1) 65 | 2. Predicting Materials Properties with Little Data Using Shotgun Transfer Learning. [ACS Cent. Sci.](https://pubs.acs.org/doi/full/10.1021/acscentsci.9b00804) 66 | 3. Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction. [arXiv](https://arxiv.org/abs/1712.02734) 67 | 4. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. [Science Advances](https://advances.sciencemag.org/content/5/8/eaav6490). [Github](https://github.com/aiqm/aimnet) 68 | 5. Deep Transferable Compound Representation across Domains and Tasks for Low Data Drug Discovery. [J. Chem. Inf. Model.](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00626) 69 | 6. An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction. [arXiv](https://arxiv.org/abs/1812.09073) 70 | 7. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. [ChemRxiv](https://chemrxiv.org/articles/Outsmarting_Quantum_Chemistry_Through_Transfer_Learning/6744440) 71 | 8. SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery. [https://arxiv.org/abs/1911.04738](https://arxiv.org/abs/1911.04738). [Github](https://github.com/DSPsleeporg/smiles-transformer) 72 | 9. Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition. [J. Chem. Inf. Model.](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.7b00616) 73 | 10. Multi-channel PINN: investigating scalable and transferable neural networks for drug discovery. [J. Cheminform.](https://link.springer.com/article/10.1186/s13321-019-0368-1) 74 | 11. Distributed Representation of Chemical Fragments. [ACS Omega](https://pubs.acs.org/doi/abs/10.1021/acsomega.7b02045) 75 | 12. Pre-training Graph Neural Networks. [arXiv](https://arxiv.org/abs/1905.12265) 76 | 13. FP2VEC: a new molecular featurizer for learning molecular properties. [Bioinformatics](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz307/5487389) 77 | 14. Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction. [arXiv](https://arxiv.org/abs/1908.06760) 78 | 15. DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks. [arXiv](https://arxiv.org/abs/1806.07537) 79 | 16. Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism. [J. Med. Chem.](https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b00959) 80 | --------------------------------------------------------------------------------