970 million druglike small molecules for virtual screening in the chemical universe database GDB-13.
By Lorenz C. Blum, Jean-Louis Reymond. 2009
831 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology, Organic Chemistry, Computational Chemistry & Drug Discovery
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- K. Müller
- Risi Kondor
- Michael Gastegger
- Kristof T. Schütt
- Oliver T. Unke
- Alexandre Tkatchenko
- Mario Geiger
- Stefan Chmiela
In Molecular Biology
- K. Müller
- Risi Kondor
- Michael Gastegger
- Kristof T. Schütt
- Oliver T. Unke
- Jure Leskovec
- Mario Geiger
- Marinka Zitnik
In Organic Chemistry
- Kristof T. Schütt
- K. Müller
- Oliver T. Unke
- Stefan Chmiela
- Huziel E. Sauceda
- Michael Gastegger
- Risi Kondor
- Alexandre Tkatchenko
In Computational Chemistry & Drug Discovery
- K. Müller
- Risi Kondor
- Kristof T. Schütt
- Michael Gastegger
- Oliver T. Unke
- Alexandre Tkatchenko
- Stefan Chmiela
- Mario Geiger
Citations in the corpus
- SchNet: A continuous-filter convolutional neural network for modeling quantum interactions (2017, background): “QM9 is a widely used benchmark for the prediction of various molecular properties in equilibrium [8, 38, 39].”
- Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network (2018, methodology): “Atomization Energy Prediction Next, we apply our framework to the QM7 dataset [30, 31], where the goal is to regress over atomization energies of molecules given atomic positions (pi) and charges (zi).”
- Spherical CNNs (2018, background): “In the QM7 task (Blum and Reymond, 2009; Rupp et al., 2012) the atomization energy of molecules has to be predicted from geometry and charges.”