Publications

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

  • 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.
  • Thakkar, A.; Chadimová, V.; Bjerrum, E. J.; Engkvist, O.; Reymond, J.-L., Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chemical Science 2021, 12 (9), 3339-3349.
  • Arús-Pous, J. Exploring the chemical space using enumerative and deep learning approaches. PhD, University of Bern, 2020.
  • 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.
  • Blaschke, T.; Arús-Pous, J.; Chen, H.; Margreitter, C.; Tyrchan, C.; Engkvist, O.; Papadopoulos, K.; Patronov, A., REINVENT 2.0: An AI Tool for De Novo Drug Design. J. Chem. Inf. Model. 2020, 60 (12), 5918-5922.
  • Blaschke, T.; Engkvist, O.; Bajorath, J.; Chen, H., Memory-assisted reinforcement learning for diverse molecular de novo design. J. Cheminform. 2020, 12 (1), 68.
  • 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.
  • 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.
  • Iqbal, J.; Vogt, M.; Bajorath, J., Activity landscape image analysis using convolutional neural networks. J. Cheminform. 2020, 12 (1), 34.
  • Karpov, P.; Godin, G.; Tetko, I. V., Transformer-CNN: Swiss knife for QSAR modeling and interpretation. J. Cheminform. 2020, 12 (1), 17.
  • 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.
  • 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.
  • 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.
  • 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.
  • Rodríguez-Pérez, R. Machine Learning Methodologies for Interpretable Compound Activity Predictions. PhD, Rheinischen Friedrich-Wilhelms-Universität Bonn, 2020.
  • 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.
  • 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.
  • Tetko, I. V.; Engkvist, O., From Big Data to Artificial Intelligence: chemoinformatics meets new challenges. J. Cheminform. 2020, 12 (1), 74.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Zhang, X. Exploration of synthetically accessible chemical space by de novo design. PhD, ETH Zürich, 2020.
  • Arús-Pous, J.; Awale, M.; Probst, D.; Reymond, J. L., Exploring Chemical Space with Machine Learning. Chimia (Aarau) 2019, 73 (12), 1018-1023.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Rodríguez-Pérez, R.; Bajorath, J., Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors. ACS Omega 2019, 4 (2), 4367-4375.
  • 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.
  • 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.
  • 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.
  • 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.
  • Arús-Pous, J.; Probst, D.; Reymond, J. L., Deep Learning Invades Drug Design and Synthesis. Chimia (Aarau) 2018, 72 (1), 70-71.
  • Blaschke, T.; Olivecrona, M.; Engkvist, O.; Bajorath, J.; Chen, H., Application of Generative Autoencoder inDe NovoMolecular Design. Mol. Inf. 2018, 37 (1-2), 1700123.
  • 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.
  • 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.
  • 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.
  • Pinzi, L.; Caporuscio, F.; Rastelli, G., Selection of protein conformations for structure-based polypharmacology studies. Drug Discov. Today 2018, 23 (11), 1889-1896.
  • 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.
  • 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.
  • Schneider, G., Automating drug discovery. Nat. Rev. Drug Discov. 2018, 17 (2), 97-113.
  • 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.
  • 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.
  • 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.
  • Olivecrona, M.; Blaschke, T.; Engkvist, O.; Chen, H., Molecular de-novo design through deep reinforcement learning. J. Cheminform. 2017, 9 (1), 48.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.