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)


1. Tetko, I. V.; Engkvist, O.; Koch, U.; Reymond, J. L.; Chen, H., BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry. Mol Inform 2016,  35(11-12):615-621 (Open Access).

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).