| Opportunities and obstacles for deep learning in biology and medicine T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Journal of The Royal Society Interface 15 (141), 20170387, 2018 | 1671 | 2018 |
| Planning chemical syntheses with deep neural networks and symbolic AI MHS Segler, M Preuss, MP Waller Nature 555 (7698), 604-610, 2018 | 1352 | 2018 |
| Generating focused molecule libraries for drug discovery with recurrent neural networks MHS Segler, T Kogej, C Tyrchan, MP Waller ACS central science 4 (1), 120-131, 2018 | 1164 | 2018 |
| GuacaMol: benchmarking models for de novo molecular design N Brown, M Fiscato, MHS Segler, AC Vaucher Journal of chemical information and modeling 59 (3), 1096-1108, 2019 | 529 | 2019 |
| Neural‐symbolic machine learning for retrosynthesis and reaction prediction MHS Segler, MP Waller Chemistry–A European Journal 23 (25), 5966-5971, 2017 | 436 | 2017 |
| Modelling Chemical Reasoning to Predict and Invent Reactions MHS Segler, MP Waller Chemistry - A European Journal 23 (25), 6118-6128, 2016 | 181 | 2016 |
| Machine learning the ropes: principles, applications and directions in synthetic chemistry F Strieth-Kalthoff, F Sandfort, MHS Segler, F Glorius Chemical Society Reviews 49 (17), 6154-6168, 2020 | 148 | 2020 |
| Artificial intelligence in drug discovery MA Sellwood, M Ahmed, MHS Segler, N Brown Future medicinal chemistry 10 (17), 2025-2028, 2018 | 94 | 2018 |
| A model to search for synthesizable molecules J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in Neural Information Processing Systems 32, 2019 | 87 | 2019 |
| Exploring deep recurrent models with reinforcement learning for molecule design D Neil, M Segler, L Guasch, M Ahmed, D Plumbley, M Sellwood, N Brown | 74 | 2018 |
| Molecular representation learning with language models and domain-relevant auxiliary tasks B Fabian, T Edlich, H Gaspar, M Segler, J Meyers, M Fiscato, M Ahmed arXiv preprint arXiv:2011.13230, 2020 | 72 | 2020 |
| A generative model for electron paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato arXiv preprint arXiv:1805.10970, 2018 | 64* | 2018 |
| Defactor: Differentiable edge factorization-based probabilistic graph generation R Assouel, M Ahmed, MH Segler, A Saffari, Y Bengio arXiv preprint arXiv:1811.09766, 2018 | 57 | 2018 |
| Improving few-and zero-shot reaction template prediction using modern hopfield networks P Seidl, P Renz, N Dyubankova, P Neves, J Verhoeven, JK Wegner, ... Journal of chemical information and modeling 62 (9), 2111-2120, 2022 | 48* | 2022 |
| Barking up the right tree: an approach to search over molecule synthesis dags J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in neural information processing systems 33, 6852-6866, 2020 | 44 | 2020 |
| Fs-mol: A few-shot learning dataset of molecules M Stanley, JF Bronskill, K Maziarz, H Misztela, J Lanini, M Segler, ... Thirty-fifth Conference on Neural Information Processing Systems Datasets …, 2021 | 42 | 2021 |
| Learning to extend molecular scaffolds with structural motifs K Maziarz, H Jackson-Flux, P Cameron, F Sirockin, N Schneider, N Stiefl, ... arXiv preprint arXiv:2103.03864, 2021 | 41 | 2021 |
| Towards" alphachem": Chemical synthesis planning with tree search and deep neural network policies M Segler, M Preuß, MP Waller arXiv preprint arXiv:1702.00020, 2017 | 38 | 2017 |
| Evaluation guidelines for machine learning tools in the chemical sciences A Bender, N Schneider, M Segler, W Patrick Walters, O Engkvist, ... Nature Reviews Chemistry 6 (6), 428-442, 2022 | 32 | 2022 |
| Silver-catalyzed 1, 3-dipolar cycloaddition of azomethine ylides with β-boryl acrylates A Lopez-Perez, M Segler, J Adrio, JC Carretero The Journal of Organic Chemistry 76 (6), 1945-1948, 2011 | 29 | 2011 |
Mark P WallerPending.AI在 pending.ai 的电子邮件经过验证
