Publications

See full list of articles at Google Scholar and also special issue in J. Cheminformatics.Big Data in Chemistry

  1. Tetko, I. V.; Engkvist, O., From Big Data to Artificial Intelligence: chemoinformatics meets new challenges. J. Cheminform. 2020, 12 (1), 74.
  2. Engkvist, O.; Arús-Pous, J.; Bjerrum, E. J.; Chen, H., Chapter 13 Molecular De Novo Design Through Deep Generative Models. In Artificial Intelligence in Drug Discovery, The Royal Society of Chemistry: 2021; pp 272-300.
  3. Arús-Pous, J. Exploring the chemical space using enumerative and deep learning approaches. PhD, University of Bern, 2020.
  4. Arús-Pous, J.; Patronov, A.; Bjerrum, E. J.; Tyrchan, C.; Reymond, J.-L.; Chen, H.; Engkvist, O., SMILES-based deep generative scaffold decorator for de-novo drug design. J. Cheminform. 2020, 12 (1), 38.
  5. Blaschke, T.; Engkvist, O.; Bajorath, J.; Chen, H., Memory-assisted reinforcement learning for diverse molecular de novo design. J. Cheminform. 2020, 12 (1), 68.
  6. David, L.; Thakkar, A.; Mercado, R.; Engkvist, O., Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminform. 2020, 12 (1), 56.
  7. Genheden, S.; Thakkar, A.; Chadimová, V.; Reymond, J.-L.; Engkvist, O.; Bjerrum, E., AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J. Cheminform. 2020, 12 (1), 70.
  8. Iqbal, J.; Vogt, M.; Bajorath, J., Activity landscape image analysis using convolutional neural networks. J. Cheminform. 2020, 12 (1), 34.
  9. Karpov, P.; Godin, G.; Tetko, I. V., Transformer-CNN: Swiss knife for QSAR modeling and interpretation. J. Cheminform. 2020, 12 (1), 17.
  10. Kotsias, P.-C.; Arús-Pous, J.; Chen, H.; Engkvist, O.; Tyrchan, C.; Bjerrum, E. J., Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. Nature Machine Intelligence 2020, 2 (5), 254-265.
  11. Lin, A.; Baskin, I. I.; Marcou, G.; Horvath, D.; Beck, B.; Varnek, A., Parallel Generative Topographic Mapping: an Efficient Approach for Big Data Handling. Mol. Inf. 2020, 39, e2000009.
  12. Lin, A.; Beck, B.; Horvath, D.; Marcou, G.; Varnek, A., Diversifying chemical libraries with generative topographic mapping. J. Comput. Aided. Mol. Des. 2020, 34 (7), 805-815.
  13. Muratov, E. N.; Bajorath, J.; Sheridan, R. P.; Tetko, I. V.; Filimonov, D.; Poroikov, V.; Oprea, T. I.; Baskin, I. I.; Varnek, A.; Roitberg, A.; Isayev, O.; Curtarolo, S.; Fourches, D.; Cohen, Y.; Aspuru-Guzik, A.; Winkler, D. A.; Agrafiotis, D.; Cherkasov, A.; Tropsha, A., QSAR without borders. Chem Soc Rev 2020, 49 (11), 3525-3564.
  14. Rodríguez-Pérez, R. Machine Learning Methodologies for Interpretable Compound Activity Predictions. PhD, Rheinischen Friedrich-Wilhelms-Universität Bonn, 2020.
  15. Rodriguez-Perez, R.; Bajorath, J., Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided. Mol. Des. 2020, 34 (10), 1013-1026.
  16. Rodríguez-Pérez, R.; Miljković, F.; Bajorath, J., Assessing the information content of structural and protein–ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning. J. Cheminform. 2020, 12 (1), 36.
  17. Tetko, I. V.; Karpov, P.; Van Deursen, R.; Godin, G., State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat. Comm. 2020, 11 (1), 1-11.
  18. Thakkar, A.; Kogej, T.; Reymond, J.-L.; Engkvist, O.; Bjerrum, E. J., Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain. Chemical Science 2020, 11 (1), 154-168.
  19. Thakkar, A.; Selmi, N.; Reymond, J.-L.; Engkvist, O.; Bjerrum, E. J., “Ring Breaker”: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space. J. Med. Chem. 2020, 63 (16), 8791-8808.
  20. van Deursen, R.; Ertl, P.; Tetko, I. V.; Godin, G., GEN: highly efficient SMILES explorer using autodidactic generative examination networks. J. Cheminform. 2020, 12 (1), 22.
  21. Withnall, M.; Lindelöf, E.; Engkvist, O.; Chen, H., Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction. J. Cheminform. 2020, 12 (1), 1.
  22. Arús-Pous, J.; Awale, M.; Probst, D.; Reymond, J. L., Exploring Chemical Space with Machine Learning. Chimia (Aarau) 2019, 73 (12), 1018-1023.
  23. Arús-Pous, J.; Blaschke, T.; Ulander, S.; Reymond, J. L.; Chen, H.; Engkvist, O., Exploring the GDB-13 chemical space using deep generative models. J. Cheminform. 2019, 11 (1), 20.
  24. Arús-Pous, J.; Johansson, S.; Prykhodko, O.; Bjerrum, E. J.; Tyrchan, C.; Reymond, J.-L.; Chen, H.; Engkvist, O. In Improving Deep Generative Models with Randomized SMILES, Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, Münich, 17th - 19th September 2019; Tetko, I. V.; Kůrková, V.; Karpov, P.; Theis, F., Eds. Springer International Publishing: Münich, 2019; pp 747-751.
  25. Arús-Pous, J.; Johansson, S. V.; Prykhodko, O.; Bjerrum, E. J.; Tyrchan, C.; Reymond, J.-L.; Chen, H.; Engkvist, O., Randomized SMILES strings improve the quality of molecular generative models. J. Cheminform. 2019, 11 (1), 71.
  26. Blaschke, T.; Miljković, F.; Bajorath, J., Prediction of Different Classes of Promiscuous and Nonpromiscuous Compounds Using Machine Learning and Nearest Neighbor Analysis. ACS Omega 2019, 4 (4), 6883-6890.
  27. David, L.; Arús-Pous, J.; Karlsson, J.; Engkvist, O.; Bjerrum, E. J.; Kogej, T.; Kriegl, J. M.; Beck, B.; Chen, H., Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research. Front. Pharmacol. 2019, 10, 1303.
  28. David, L.; Walsh, J.; Bajorath, J.; Engkvist, O. In Detection of Frequent-Hitters Across Various HTS Technologies, Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, Münich, 17th - 19th September 2019; Tetko, I. V.; Kůrková, V.; Karpov, P.; Theis, F., Eds. Springer International Publishing: Münich, 2019; pp 842-844.
  29. David, L.; Walsh, J.; Sturm, N.; Feierberg, I.; Nissink, J. W. M.; Chen, H.; Bajorath, J.; Engkvist, O., Identification of Compounds That Interfere with High-Throughput Screening Assay Technologies. ChemMedChem 2019, 14 (20), 1795-1802.
  30. Ghosh, D.; Tetko, I.; Klebl, B.; Nussbaumer, P.; Koch, U. In Analysis and Modelling of False Positives in GPCR Assays, Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, Münich, 17th - 19th September 2019; Tetko, I. V.; Kůrková, V.; Karpov, P.; Theis, F., Eds. Springer International Publishing: Münich, 2019; pp 764-770.
  31. Karpov, P.; Godin, G.; Tetko, I. V. In A Transformer Model for Retrosynthesis, Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, Münich, 17th - 19th September 2019; Tetko, I. V.; Kůrková, V.; Karpov, P.; Theis, F., Eds. Springer International Publishing: Münich, 2019; pp 817-830.
  32. Laufkötter, O.; Miyao, T.; Bajorath, J., Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents. ACS Omega 2019, 4 (12), 15304-15311.
  33. Laufkötter, O.; Sturm, N.; Bajorath, J.; Chen, H.; Engkvist, O., Combining structural and bioactivity-based fingerprints improves prediction performance and scaffold hopping capability. J. Cheminform. 2019, 11 (1), 54.
  34. Lin, A. Cartographie topographique générative : un outil puissant pour la visualisation, l'analyse et la modélisation de données chimiques volumineuses. PhD, University of Strasbourg, 2019.
  35. Lin, A.; Beck, B.; Horvath, D.; Varnek, A. In Diversify Libraries Using Generative Topographic Mapping, Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, Münich, 17th - 19th September 2019; Tetko, I. V.; Kůrková, V.; Karpov, P.; Theis, F., Eds. Springer International Publishing: Münich, 2019; pp 839-841.
  36. 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.
  37. Miljković, F.; Rodríguez-Pérez, R.; Bajorath, J., Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes. J. Med. Chem. 2019, 63 (16), 8738-8748.
  38. Prykhodko, O.; Johansson, S. V.; Kotsias, P.-C.; Arús-Pous, J.; Bjerrum, E. J.; Engkvist, O.; Chen, H., A de novo molecular generation method using latent vector based generative adversarial network. J. Cheminform. 2019, 11 (1), 74.
  39. Rodríguez-Pérez, R.; Bajorath, J., Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values. J. Med. Chem. 2019, 63 (16), 8761-8777.
  40. Rodríguez-Pérez, R.; Bajorath, J., Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors. ACS Omega 2019, 4 (2), 4367-4375.
  41. Sosnin, S.; Vashurina, M.; Withnall, M.; Karpov, P.; Fedorov, M.; Tetko, I. V., A Survey of Multi-task Learning Methods in Chemoinformatics. Mol. Inform. 2019, 38 (4), e1800108.
  42. Tetko, I. V.; Karpov, P.; Bruno, E.; Kimber, T. B.; Godin, G. In Augmentation Is What You Need!, Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, Münich, 17th - 19th September 2019; Tetko, I. V.; Kůrková, V.; Karpov, P.; Theis, F., Eds. Springer International Publishing: Münich, 2019; pp 831-835.
  43. Thakkar, A.; Bjerrum, E. J.; Engkvist, O.; Reymond, J.-L. In Neural Network Guided Tree-Search Policies for Synthesis Planning, Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, Münich, 17th - 19th September 2019; Tetko, I. V.; Kůrková, V.; Karpov, P.; Theis, F., Eds. Springer International Publishing: Münich, 2019; pp 721-724.
  44. Withnall, M.; Lindelöf, E.; Engkvist, O.; Chen, H. In Attention and Edge Memory Convolution for Bioactivity Prediction, Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, Münich, 17th - 19th September 2019; Tetko, I. V.; Kůrková, V.; Karpov, P.; Theis, F., Eds. Springer International Publishing: Münich, 2019; pp 752-757.
  45. Zhang, X. Exploration of synthetically accessible chemical space by de novo design. PhD, ETH Zürich, 2019.
  46. Arús-Pous, J.; Probst, D.; Reymond, J. L., Deep Learning Invades Drug Design and Synthesis. Chimia (Aarau) 2018, 72 (1), 70-71.
  47. Blaschke, T.; Olivecrona, M.; Engkvist, O.; Bajorath, J.; Chen, H., Application of Generative Autoencoder in De Novo Molecular Design. Mol. Inform. 2018, 37 (1-2).
  48. 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.
  49. Ghosh, D.; Koch, U.; Hadian, K.; Sattler, M.; Tetko, I. V., Luciferase Advisor: High-Accuracy Model To Flag False Positive Hits in Luciferase HTS Assays. J. Chem. Inf. Model. 2018, 58 (5), 933-942.
  50. Lin, A.; Horvath, D.; Afonina, V.; Marcou, G.; Reymond, J. L.; Varnek, A., Mapping of the Available Chemical Space versus the Chemical Universe of Lead-Like Compounds. ChemMedChem 2018, 13 (6), 540-554.
  51. Pinzi, L.; Caporuscio, F.; Rastelli, G., Selection of protein conformations for structure-based polypharmacology studies. Drug Discov. Today 2018, 23 (11), 1889-1896.
  52. Rodríguez-Pérez, R.; Bajorath, J., Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data. ACS Omega 2018, 3 (9), 12033-12040.
  53. Rodríguez-Pérez, R.; Miyao, T.; Jasial, S.; Vogt, M.; Bajorath, J., Prediction of Compound Profiling Matrices Using Machine Learning. ACS Omega 2018, 3 (4), 4713-4723.
  54. Schneider, G., Automating drug discovery. Nat. Rev. Drug Discov. 2018, 17 (2), 97-113.
  55. Withnall, M.; Chen, H.; Tetko, I. V., Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective. ChemMedChem 2018, 13 (6), 599-606.
  56. Awale, M.; Visini, R.; Probst, D.; Arús-Pous, J.; Reymond, J. L., Chemical Space: Big Data Challenge for Molecular Diversity. Chimia (Aarau) 2017, 71 (10), 661-666.
  57. March-Vila, E.; Pinzi, L.; Sturm, N.; Tinivella, A.; Engkvist, O.; Chen, H.; Rastelli, G., On the Integration of In Silico Drug Design Methods for Drug Repurposing. Front. Pharmacol. 2017, 8, 298.
  58. Olivecrona, M.; Blaschke, T.; Engkvist, O.; Chen, H., Molecular de-novo design through deep reinforcement learning. J. Cheminform. 2017, 9 (1), 48.
  59. Rodríguez-Pérez, R.; Vogt, M.; Bajorath, J., Support Vector Machine Classification and Regression Prioritize Different Structural Features for Binary Compound Activity and Potency Value Prediction. ACS Omega 2017, 2 (10), 6371-6379.
  60. Rodríguez-Pérez, R.; Vogt, M.; Bajorath, J., Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds. J. Chem. Inf. Model. 2017, 57 (4), 710-716.
  61. Visini, R.; Arús-Pous, J.; Awale, M.; Reymond, J. L., Virtual Exploration of the Ring Systems Chemical Universe. J. Chem. Inf. Model. 2017, 57 (11), 2707-2718.
  62. 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.
  63. 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.