Interpretability of machine learning models is critical to scientific understanding, AI safety, as well as debugging.
Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.
3 code implementations • 29 Nov 2018 • Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, Artur Kadurin, Simon Johansson, Hongming Chen, Sergey Nikolenko, Alan Aspuru-Guzik, Alex Zhavoronkov
Generative models are becoming a tool of choice for exploring the molecular space.
In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics.
Ranked #1 on Molecular Graph Generation on ZINC (QED Top-3 metric)