AkshatKumar Nigam
AkshatKumar Nigam
Verified email at stanford.edu
Cited by
Cited by
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
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
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
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, 2021
A comprehensive discovery platform for organophosphorus ligands for catalysis
T Gensch, G dos Passos Gomes, P Friederich, E Peters, T Gaudin, ...
Assigning confidence to molecular property prediction
AK Nigam, R Pollice, MFD Hurley, RJ Hickman, M Aldeghi, N Yoshikawa, ...
Expert Opinion on Drug Discovery, 1-15, 2021
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
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
AK Nigam, R Pollice, A Aspuru-Guzik
arXiv preprint arXiv:2106.04011, 2021
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
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