AkshatKumar (Akshat) Nigam

I am a PhD student at Stanford in the departments of Computer Science & Genetics. I love molecules, both big and small, calculating complex properties and leveraging machine learning tools for estimating those properties. For a detailed list of my publications, please have a look at my Google Scholar

News

• 2022.10.17: Paper published in Cell's Patterns SELFIES and the future of molecular string representations. Work done with 30 co-authors from 14 countries!.
• 2022.10.11: Paper published in Nature Review Physics On Scientific Understanding with Artificial Intelligence. A wild view into the future of how AI could contribute to the essentail aims of science.
• 2022.09.26: Release of our Preprint: Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design.
• 2022.06.02: Our article Curiosity in exploring chemical spaces: Intrinsic rewards for molecular reinforcement learning has been accepted in MLST!.
• 2022.05.27: I am extremely happy to be the recepient of the BioX SGIF Fellowship!.
• 2022.04.01: Release of our Preprint: On scientific understanding with artificial intelligence.
• 2022.03.31: Release of our Preprint: SELFIES and the future of molecular string representations, (collaboration with 31 coauthors from 10 different countries!).
• 2022.01.19: Paper published in Digital Discovery on our inverse design algorithm, JANUS.
• 2022.01.12: Paper published in JACS on "A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis".
• 2021.04.29: Review published in Expert Opinion on Drug Discovery on "Assigning confidence to molecular property prediction".
• 2021.04.12: Paper published in Chemical Science on performing extremely efficient searches in the chemical space using STONED-SELFIES.
• 2021.02.02: Review published in Acount of Chemical Research on "Data-Driven Strategies for Accelerated Materials Design".
• 2020.04.23: Paper published in ICLR on "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"
• 2020.10.28: Paper published in Machine Learning: Science and Technology on SELFIES!

Experience

Education

Publications:

1. Nigam, AkshatKumar, et al. "Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design." arXiv preprint arXiv:2209.12487 (2022).

2. Krenn, Mario, et al. "On scientific understanding with artificial intelligence." arXiv preprint arXiv:2204.01467 (2022); Now in Nature Reviews Physics
3. Krenn, Mario, et al. "SELFIES and the future of molecular string representations." arXiv preprint arXiv:2204.00056 (2022); Now in Cell's Patterns
4. Nigam, AkshatKumar, Robert Pollice, and Alán Aspuru-Guzik. "Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design." Digital Discovery (2022) .

5. Nigam, AkshatKumar, et al. "Assigning confidence to molecular property prediction." Expert opinion on drug discovery 16.9 (2021): 1009-1023.

6. Gensch, Tobias, et al. "A comprehensive discovery platform for organophosphorus ligands for catalysis." Journal of the American Chemical Society 144.3 (2022): 1205-1217.

7. Pollice, Robert, et al. "Data-driven strategies for accelerated materials design." Accounts of Chemical Research 54.4 (2021): 849-860.

8. Nigam, AkshatKumar, et al. "Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES." Chemical science 12.20 (2021): 7079-7090.

9. Thiede, Luca A., et al. "Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning." arXiv preprint arXiv:2012.11293 (2020).
10. Krenn, Mario, et al. "Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation." Machine Learning: Science and Technology 1.4 (2020): 045024.
11. Nigam, AkshatKumar, et al. "Augmenting genetic algorithms with deep neural networks for exploring the chemical space." arXiv preprint arXiv:1909.11655 (2019).