663 citations · 1.5k across the 9 of their papers we have counts for
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Assigning Confidence to Molecular Property Prediction
AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley +6
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design. Recentl…
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
Luca A. Thiede, Mario Krenn, AkshatKumar Nigam +1
Computer-aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning, and deep learning, in particular, have been topics where…
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam +2
The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncove…