| Identifying at-risk students in massive open online courses J He, J Bailey, B Rubinstein, R Zhang Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015 | 267 | 2015 |
| Chemformer: a pre-trained transformer for computational chemistry R Irwin, S Dimitriadis, J He, EJ Bjerrum Machine Learning: Science and Technology 3 (1), 015022, 2022 | 237 | 2022 |
| Molecular optimization by capturing chemist’s intuition using deep neural networks J He, H You, E Sandström, E Nittinger, EJ Bjerrum, C Tyrchan, ... Journal of cheminformatics 13, 1-17, 2021 | 90 | 2021 |
| Transformer-based molecular optimization beyond matched molecular pairs J He, E Nittinger, C Tyrchan, W Czechtizky, A Patronov, EJ Bjerrum, ... Journal of cheminformatics 14 (1), 18, 2022 | 47 | 2022 |
| Reinvent 4: Modern AI–driven generative molecule design HH Loeffler, J He, A Tibo, JP Janet, A Voronov, LH Mervin, O Engkvist Journal of Cheminformatics 16 (1), 20, 2024 | 36 | 2024 |
| Naive bayes classifier for positive unlabeled learning with uncertainty J He, Y Zhang, X Li, Y Wang Proceedings of the 2010 SIAM international conference on data mining, 361-372, 2010 | 33 | 2010 |
| Exploiting transitive similarity and temporal dynamics for similarity search in heterogeneous information networks J He, J Bailey, R Zhang Database Systems for Advanced Applications: 19th International Conference …, 2014 | 27 | 2014 |
| Learning naive Bayes classifiers from positive and unlabelled examples with uncertainty J He, Y Zhang, X Li, P Shi International journal of systems science 43 (10), 1805-1825, 2012 | 27 | 2012 |
| Bayesian classifiers for positive unlabeled learning J He, Y Zhang, X Li, Y Wang International Conference on Web-Age Information Management, 81-93, 2011 | 19 | 2011 |
| MOOCs meet measurement theory: a topic-modelling approach J He, B Rubinstein, J Bailey, R Zhang, S Milligan, J Chan Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 17 | 2016 |
| Implications of additivity and nonadditivity for machine learning and deep learning models in drug design K Kwapien, E Nittinger, J He, C Margreitter, A Voronov, C Tyrchan ACS omega 7 (30), 26573-26581, 2022 | 16 | 2022 |
| Transformer neural network for structure constrained molecular optimization J He, F Mattsson, M Forsberg, EJ Bjerrum, O Engkvist, C Tyrchan, ... | 9 | 2021 |
| Levenshtein augmentation improves performance of smiles based deep-learning synthesis prediction D Sumner, J He, A Thakkar, O Engkvist, EJ Bjerrum | 9 | 2020 |
| Molecular optimization by capturing chemist’s intuition using deep neural networks. J Cheminform 13: 26 J He, H You, E Sandström, E Nittinger, EJ Bjerrum, C Tyrchan, ... | 6 | 2021 |
| Exhaustive local chemical space exploration using a transformer model A Tibo, J He, JP Janet, E Nittinger, O Engkvist Nature Communications 15 (1), 7315, 2024 | 5 | 2024 |
| Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification Y Zhang, J Menke, J He, E Nittinger, C Tyrchan, O Koch, H Zhao Journal of Cheminformatics 15 (1), 75, 2023 | 4 | 2023 |
| Validity: a framework for cross-disciplinary collaboration in mining indicators of learning from MOOC forums S Milligan, J He, J Bailey, R Zhang, BIP Rubinstein proceedings of the sixth international conference on learning analytics …, 2016 | 3 | 2016 |
| Evaluation of reinforcement learning in transformer-based molecular design J He, A Tibo, JP Janet, E Nittinger, C Tyrchan, W Czechtizky, O Engkvist Journal of Cheminformatics 16 (1), 95, 2024 | 2 | 2024 |
| Transformer neural network-based molecular optimization using general transformations J He, E Nittinger, C Tyrchan, W Czechtizky, A Patronov, EJ Bjerrum, ... | 2 | 2021 |
| TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs J He, BIP Rubinstein, J Bailey, R Zhang, S Milligan arXiv preprint arXiv:1607.08720, 2016 | 1 | 2016 |
