1. Rodríguez-Pérez R, Bajorath J,  Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors. ACS Omega, 2019, 4 (2), 4367–4375

2. Lin A, Horvath D, Marcou G, Beck B, Varnek A,  Multi-task generative topographic mapping in virtual screening. J Comput Aided Mol Des, 2019, 33(3):331-343

3. Arús-Pous J, Blaschke T, Ulander S, Reymond JL, Chen H, Engkvist O,  Exploring the GDB-13 chemical space using deep generative models. J Cheminform, 2019, 11(1):20


1. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T,  The rise of deep learning in drug discovery. Drug Discov Today, 2018, 23(6):1241-1250

2. Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H,  Application of Generative Autoencoder in De Novo Molecular Design. Molecular informatics, 2018, 37 (1-2), 1700123

3. Pinzi L, Caporuscio F., Rastelli G.,  Selection of protein conformations for structure-based polypharmacology studies. Drug Discov. Today, 2018, 23, 1889-1896

4. Ghosh D, Koch U, Hadian K, Sattler M, Tetko IV,  Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays. J Chem Inf Model, 2018, 58 (5), 933-942

5. Rodríguez-Pérez R, Miyao T, Jasial S, Vogt M, Bajorath J,  Prediction of compound profiling matrices using machine learning. ACS Omega, 2018, 3 (6), 4713-4723

6. Rodríguez-Pérez R, Bajorath J,  Prediction of compound profiling matrices, part II: relative performance of multi-task deep learning and random forest classification on the basis of varying amounts of training data. ACS Omega, 2018, 3 (6), 12033-12040

7. Lin A, Horvath D, Afonina V, Marcou G, Reymond JL, Varnek A,  Mapping of the Available Chemical Space versus the Chemical Universe of Lead-Like Compounds. ChemMedChem, 2018, 13 (6), 540-554

8. Arús-Pous K, Probst D, Reymond JL,  Deep Learning Invades Drug Design and Synthesis (commentary). Chimia, 2018, J58 (9)

9. Sosnin S, Vashurina M, Withnall M, Karpov P, Fedorov M, Tetko IV,  A Survey of Multi-Task Learning Methods in Chemoinformatics. Mol Inform. 2018 Nov 28. Review.


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. 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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