20 citations · 20 across the 1 of their papers we have counts for
4 papers
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
AkshatKumar Nigam, Robert Pollice, Alan Aspuru-Guzik
Inverse molecular design, i.e., designing molecules with specific target properties, can be posed as an optimization problem. High-dimensional optimization tasks in the natural sci…
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…
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
AkshatKumar Nigam, Pascal Friederich, Mario Krenn +1
Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistr…
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…