AkshatKumar (Akshat) Nigam

Stanford University · Department of Computer Science & Genetics · akshat98@stanford.edu

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



Graduate Student (PhD)

Kundaje & Bassik Labs, Stanford University.

Computer-aided experimental design for discovery of protein function.

March 2021 - Present

Research Assistant

Aspuru-Guzik Research Group (Matterlab), University of Toronto

Design of algorithms for discovering novel small molecules (photovoltaics & drugs).

Jan 2019 - March 2021

Research intern

Vector Institute for Artificial Intelligence

Reconstructing quantum states using generative models.

Sept 2018 - December 2018


Stanford University

PhD Student
Department of Computer Science & Genetics
Advised by: Anshul Kundaje & Michael Bassik
March 2021 - Current

University of Toronto, St. George

Undergraduate Degree in the Department of Computer Science
Sept 2016 - Oct 2020


  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).