| 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 | 593 | 2018 |
| Planning chemical syntheses with deep neural networks and symbolic AI MHS Segler, M Preuss, MP Waller Nature 555 (7698), 604-610, 2018 | 503 | 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 | 410 | 2018 |
| Neural‐symbolic machine learning for retrosynthesis and reaction prediction MHS Segler, MP Waller Chemistry–A European Journal 23 (25), 5966-5971, 2017 | 186 | 2017 |
| Modelling chemical reasoning to predict and invent reactions MHS Segler, MP Waller Chemistry–A European Journal 23 (25), 6118-6128, 2017 | 102 | 2017 |
| 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 | 95 | 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 | 27 | 2018 |
| A generative model for electron paths J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato arXiv preprint arXiv:1805.10970, 2018 | 24* | 2018 |
| Opportunities and obstacles for deep learning in biology and medicine. bioRxiv T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... New York: Wiley, 2017 | 23 | 2017 |
| 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 | 20 | 2017 |
| Defactor: Differentiable edge factorization-based probabilistic graph generation R Assouel, M Ahmed, MH Segler, A Saffari, Y Bengio arXiv preprint arXiv:1811.09766, 2018 | 19 | 2018 |
| Silver-catalyzed 1, 3-dipolar cycloaddition of azomethine ylides with β-boryl acrylates A López-Pérez, M Segler, J Adrio, JC Carretero The Journal of organic chemistry 76 (6), 1945-1948, 2011 | 19 | 2011 |
| Artificial intelligence in drug discovery MA Sellwood, M Ahmed, MHS Segler, N Brown Future medicinal chemistry 10 (17), 2025-2028, 2018 | 18 | 2018 |
| Dehydrogenative TEMPO‐Mediated Formation of Unstable Nitrones: Easy Access to N‐Carbamoyl Isoxazolines A Gini, M Segler, D Kellner, OG Mancheno Chemistry–A European Journal 21 (34), 12053-12060, 2015 | 17 | 2015 |
| A model to search for synthesizable molecules J Bradshaw, B Paige, MJ Kusner, M Segler, JM Hernández-Lobato Advances in Neural Information Processing Systems, 7937-7949, 2019 | 14 | 2019 |
| 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 | 1 | 2020 |
| World programs for model-based learning and planning in compositional state and action spaces MHS Segler arXiv preprint arXiv:1912.13007, 2019 | 1 | 2019 |
| Opportunities and obstacles for deep learning in biology and medicine: 2019 update T Ching, DS Himmelstein, BK Beaulieu-Jones, AA Kalinin, BT Do, ... Manubot, 2019 | | 2019 |
| Generating molecules via chemical reactions J Bradshaw, MJ Kusner, B Paige, MHS Segler, JM Hernández-Lobato Deep Generative Models for Highly Structured Data, DGS@ ICLR 2019 Workshop 7, 2019 | | 2019 |
| Robogenic chemistry S Mcananama-Brereton, M Segler, M Waller ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 256, 2018 | | 2018 |
Mark P WallerPending.AIVerified email at pending.ai
