AkshatKumar Nigam
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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
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
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
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
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
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
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
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
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
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
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
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
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
Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network
AK Nigam, R Pollice, P Friederich, A Aspuru-Guzik
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
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
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
The Impact of Genomic Variation on Function (IGVF) Consortium
I Consortium
arXiv:2307.13708, 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
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