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 | 2032 | 2018 |
Planning chemical syntheses with deep neural networks and symbolic AI MHS Segler, M Preuss, MP Waller Nature 555 (7698), 604-610, 2018 | 1681 | 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 | 1461 | 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 | 749 | 2019 |
Neural‐symbolic machine learning for retrosynthesis and reaction prediction MHS Segler, MP Waller Chemistry–A European Journal 23 (25), 5966-5971, 2017 | 558 | 2017 |
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 | 207 | 2020 |
Modelling Chemical Reasoning to Predict and Invent Reactions MHS Segler, MP Waller Chemistry - A European Journal 23 (25), 6118-6128, 2016 | 198 | 2016 |
Artificial intelligence in drug discovery MA Sellwood, M Ahmed, MHS Segler, N Brown Future medicinal chemistry 10 (17), 2025-2028, 2018 | 135 | 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 | 127 | 2020 |
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 | 113 | 2019 |
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 | 100* | 2022 |
Exploring deep recurrent models with reinforcement learning for molecule design D Neil, M Segler, L Guasch, M Ahmed, D Plumbley, M Sellwood, N Brown | 84 | 2018 |
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 | 79 | 2022 |
A generative model for electron paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato arXiv preprint arXiv:1805.10970, 2018 | 78* | 2018 |
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 | 77 | 2021 |
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 | 75 | 2021 |
Artificial intelligence for natural product drug discovery MW Mullowney, KR Duncan, SS Elsayed, N Garg, JJJ van der Hooft, ... Nature Reviews Drug Discovery 22 (11), 895-916, 2023 | 72 | 2023 |
Defactor: Differentiable edge factorization-based probabilistic graph generation R Assouel, M Ahmed, MH Segler, A Saffari, Y Bengio arXiv preprint arXiv:1811.09766, 2018 | 66 | 2018 |
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 | 60 | 2020 |
RetroGNN: fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software CH Liu, M Korablyov, S Jastrzebski, P Włodarczyk-Pruszynski, Y Bengio, ... Journal of Chemical Information and Modeling 62 (10), 2293-2300, 2022 | 54* | 2022 |