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
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
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
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
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
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, S Selim Çınaroğlu, C Secker, ...
bioRxiv, 2023.04. 25.537981, 2023
Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network
AK Nigam, R Pollice, P Friederich, A Aspuru-Guzik
Chemical Science, 2024
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
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
Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS
MG Vakili, C Gorgulla, AK Nigam, D Bezrukov, D Varoli, A Aliper, ...
arXiv preprint arXiv:2402.08210, 2024
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
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
Allosteric inhibition of tRNA synthetase Gln4 by N-pyrimidinyl-β-thiophenylacrylamides exerts highly selective antifungal activity
E Puumala, D Sychantha, E Lach, S Reeves, S Nabeela, M Fogal, ...
Cell Chemical Biology, 2024
Application of established computational techniques to identify potential SARS-CoV-2 Nsp14-MTase inhibitors in low data regimes
AK Nigam, MFD Hurley, F Lu, E Konkoľová, M Klima, J Trylčová, R Pollice, ...
Digital Discovery, 2024
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Articles 1–20