Machine Learning Methods for Property Prediction in Chemoinformatics: Quo Vadis?

By Alexandre Varnek, Igor I. Baskin. 2012

220 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Spectroscopy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Christian Tyrchan
2. Mark P. Waller
3. Marwin H. S. Segler
4. Thierry Kogej

In Computational Theory and Mathematics

1. Vijay S. Pande
2. Marwin H. S. Segler
3. Mark P. Waller
4. Steven M. Kearnes
5. Bharath Ramsundar
6. Patrick F. Riley
7. Christian Tyrchan
8. David E. Konerding

In Spectroscopy

1. Patrick F. Riley
2. Vijay S. Pande
3. Christian Tyrchan
4. Mark P. Waller
5. Marwin H. S. Segler
6. Thierry Kogej
7. Frank No'e

Citations in the corpus

• Massively Multitask Networks for Drug Discovery (2015, background): “Early work combined creative featurizations of molecules with off-the-shelf learning algorithms to predict drug activity (Varnek & Baskin, 2012).”
• Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks (2017, methodology): “14,15 The molecules are split into actives and inactives using a threshold on a measure for the substance e ff ectiveness. pIC 50 = − log 10 (IC 50 ) is one of the most widely used metrics for this purpose.”