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1. Rodríguez Pérez R, Vogt M, Bajorath J, Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active CompoundsJ. Chem. Inf. Model., 2017, 57 (4), pp 710–716. doi: 10.1021/acs.jcim.7b00088 (Open Access)

2. March-Vila E, Pinzi L, Sturm N, Tinivella A, Engkvist O, Chen H and Rastelli G,  On the Integration of In Silico Drug Design Methods for Drug Repurposing. Front. Pharmacol., 2017, 8:298. doi: 10.3389/fphar.2017.00298 (Open Access).

3. Olivecrona M, Blaschke T, Engkvist O and Chen H, Molecular De Novo Design through Deep Reinforcement Learning. J Cheminform, 2017, 9:4. doi:I 10.1186/s13321-017-0235-x (Open Access)

4. Withnall M; Chen H; Tetko I, Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective. ChemMedChem, 2017, 12, 1-9. doi:10.1002/cmdc.201700303 (Open Access)

5. Rodríguez-Pérez R, Vogt M, Bajorath, Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction, ACS Omega 2017, 2, 6371-6379. doi: 10.1021/acsomega.7b01079 (Open Access)

6. Visini,R, Arús-Pous J, Awale M, Reymond JL, Virtual Exploration of the Ring Systems Chemical Universe, J. Chem. Inf. Model. 2017, doi: 10.1021/acs.jcim.7b00457 (Open Access)

7. Lin A, Horvath D, Afonina V, Marcou G, Jean-Louis R, Varnek, Mapping of the Available Chemical Space versus the Chemical Universe of Lead-like Compounds. ChemMedChem 2017

8. Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H, Application of Generative Autoencoder in de Novo Molecular Design. Mol Inform 2017

9. Awale M, Visini R, Probst D, Arus-Pous J, Reymond JL, Chemical Space: Big Data Challenge for Molecular Diversity. Chimia (Aarau) 2017;71: 661-6.


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2. Tetko, I.V.; Engkvist, O.; Chen, H. Does 'Big Data' exist in medicinal chemistry, and if so, how can it be harnessed? Future Med Chem. 2016, 8(15):1801-1806 (Open Access).