AkshatKumar Nigam

According to our database, AkshatKumar Nigam authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS.

Mohammad Ghazi Vakili, Christoph Gorgulla, AkshatKumar Nigam, Dmitry Bezrukov, Daniel Varoli, Alex Aliper, Daniil Polykovskiy, Krishna M. Padmanabha Das, Jamie Snider, Anna Lyakisheva, Ardalan Hosseini Mansob, Zhong Yao, Lela Bitar, Eugene Radchenko, Xiao Ding, Jinxin Liu, Fanye Meng, Feng Ren, Yudong Cao, Igor Stagljar, Alán Aspuru-Guzik, Alex Zhavoronkov

CoRR, 2024

2023

Recent advances in the Self-Referencing Embedding Strings (SELFIES) library.

Alston Lo, Robert Pollice, AkshatKumar Nigam, Andrew D. White, Mario Krenn, Alán Aspuru-Guzik

CoRR, 2023

Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design.

AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, John Willes, Luca A. Thiede, Anshul Kundaje, Alán Aspuru-Guzik

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

SELFIES and the future of molecular string representations.

Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F. Lameiro, Dominik Lemm, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew D. White, Adamo Young, Rose Yu, Alán Aspuru-Guzik

Patterns, 2022

Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning.

Luca A. Thiede, Mario Krenn, AkshatKumar Nigam, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2022

Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design.

AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, Luca A. Thiede, Anshul Kundaje, Alán Aspuru-Guzik

CoRR, 2022

On scientific understanding with artificial intelligence.

Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, AkshatKumar Nigam, Zhenpeng Yao, Alán Aspuru-Guzik

CoRR, 2022

2021

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design.

AkshatKumar Nigam, Robert Pollice, Alán Aspuru-Guzik

CoRR, 2021

Assigning Confidence to Molecular Property Prediction.

AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley, Riley J. Hickman, Matteo Aldeghi, Naruki Yoshikawa, Seyone Chithrananda, Vincent A. Voelz, Alán Aspuru-Guzik

CoRR, 2021

2020

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation.

Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2020

Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning.

Luca A. Thiede, Mario Krenn, AkshatKumar Nigam, Alán Aspuru-Guzik

CoRR, 2020

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space.

AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry.

Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

CoRR, 2019