| Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation M Krenn, F Hase, AK Nigam, P Friederich, A Aspuru-Guzik Machine Learning: Science and Technology, 2020 | 556* | 2020 |
| Data-driven strategies for accelerated materials design R Pollice, G dos Passos Gomes, M Aldeghi, RJ Hickman, M Krenn, ... Accounts of Chemical Research 54 (4), 849-860, 2021 | 196 | 2021 |
| Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space AK Nigam, P Friederich, M Krenn, A Aspuru-Guzik International Conference on Learning Representations (ICLR)., 2020 | 124 | 2020 |
| A comprehensive discovery platform for organophosphorus ligands for catalysis T Gensch, G dos Passos Gomes, P Friederich, E Peters, T Gaudin, ... Journal of the American Chemical Society, 2021 | 108 | 2021 |
| Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES AK Nigam, R Pollice, M Krenn, G dos Passos Gomes, A Aspuru-Guzik Chemical science 12 (20), 7079-7090, 2021 | 106 | 2021 |
| On scientific understanding with artificial intelligence M Krenn, R Pollice, SY Guo, M Aldeghi, A Cervera-Lierta, P Friederich, ... Nature Reviews Physics 4 (12), 761-769, 2022 | 89 | 2022 |
| SELFIES and the future of molecular string representations M Krenn, Q Ai, S Barthel, N Carson, A Frei, NC Frey, P Friederich, ... Patterns 3 (10), 2022 | 52 | 2022 |
| Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design AK Nigam, R Pollice, A Aspuru-Guzik Digital Discovery 1 (4), 390-404, 2022 | 51* | 2022 |
| Assigning confidence to molecular property prediction AK Nigam, R Pollice, MFD Hurley, RJ Hickman, M Aldeghi, N Yoshikawa, ... Expert opinion on drug discovery 16 (9), 1009-1023, 2021 | 39 | 2021 |
| Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning LA Thiede, M Krenn, AK Nigam, A Aspuru-Guzik Machine Learning: Science and Technology 3 (3), 035008, 2020 | 35* | 2020 |
| Tartarus: A benchmarking platform for realistic and practical inverse molecular design AK Nigam, R Pollice, G Tom, K Jorner, LA Thiede, A Kundaje, ... 37th Conference on Neural Information Processing Systems (NeurIPS 2023 …, 2023 | 6 | 2023 |
| VirtualFlow 2.0-The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules C Gorgulla, AK Nigam, M Koop, SS Cinaroglu, C Secker, M Haddadnia, ... bioRxiv, 2023.04. 25.537981, 2023 | 4 | 2023 |
| Exploring the chemical space without bias: data-free molecule generation with DQN and SELFIES T Gaudin, AK Nigam, A Aspuru-Guzik NeurIPS-2019 MLPS Workshop, 0 | 2* | |
| Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network AK Nigam, R Pollice, P Friederich, A Aspuru-Guzik | 1 | 2023 |
| Recent advances in the Self-Referencing Embedding Strings (SELFIES) library A Lo, R Pollice, AK Nigam, AD White, M Krenn, A Aspuru-Guzik Digital Discovery 2, 897-908, 2023 | 1 | 2023 |
| SLC12A9 is a lysosome-detoxifying ammonium-chloride co-transporter R Levin-Konigsberg, K Mitra, AK Nigam, K Spees, P Hivare, K Liu, ... bioRxiv, 2023.05. 22.541801, 2023 | 1 | 2023 |
| Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors AK Nigam, MFD Hurley, F Li, E Konkoĭová, M Klíma, J Trylčová, R Pollice, ... bioRxiv, 2023 | | 2023 |
| The Impact of Genomic Variation on Function (IGVF) Consortium I Consortium arXiv:2307.13708, 2023 | | 2023 |
| Assessing multi-objective optimization of molecules with genetic algorithms against relevant baselines N Kusanda, G Tom, R Hickman, AK Nigam, K Jorner, A Aspuru-Guzik AI for Accelerated Materials Design NeurIPS 2022 Workshop, 2022 | | 2022 |
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Verified email at utoronto.ca
