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AkshatKumar Nigam
AkshatKumar Nigam
Verified email at stanford.edu
Title
Cited by
Cited by
Year
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
210*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
682021
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
672020
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
442021
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
192021
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
152021
Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
AK Nigam, R Pollice, A Aspuru-Guzik
Digital Discovery, 2022
12*2022
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
LA Thiede, M Krenn, AK Nigam, A Aspuru-Guzik
NeurIPS 2019, Second Workshop on Machine Learning and the Physical Sciences, 2020
102020
SELFIES and the future of molecular string representations
M Krenn, Q Ai, S Barthel, N Carson, A Frei, NC Frey, P Friederich, ...
arXiv preprint arXiv:2204.00056, 2022
12022
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
1
On scientific understanding with artificial intelligence
M Krenn, R Pollice, SY Guo, M Aldeghi, A Cervera-Lierta, P Friederich, ...
arXiv preprint arXiv:2204.01467, 2022
2022
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