Multi-parameter optimization: identifying high quality compounds with a balance of properties.

By Matthew D. Segall. 2012

166 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Immunology, Physical and Theoretical Chemistry

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

1. Gisbert Schneider
2. Jure Leskovec
3. Bowen Liu
4. Rex Ying
5. Petra Schneider
6. Matthias Zentgraf
7. W. Patrick Walters
8. Kimito Funatsu

In Immunology

1. Gisbert Schneider
2. Jure Leskovec
3. Vijay S. Pande

In Physical and Theoretical Chemistry

1. Gisbert Schneider
2. Jiaxuan You
3. Jure Leskovec
4. Vijay S. Pande
5. Bowen Liu
6. Rex Ying

Citations in the corpus

• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (2018, background): “However, the generation of novel and valid molecular graphs that can directly optimize various desired physical, chemical and biological property objectives remains to be a challenging task, since these property objectives are highly complex [37] and non-differentiable.”
• Rethinking drug design in the artificial intelligence era (2019, background): “One fundamental ingredient to modern day MOO algorithms, such as EDA, is a generative model that takes the place of mutations and crossover operations in genetic algorithms.”
• Can we really do computer-aided drug design? (2011, methodology): “However, computational approaches can be used to support decision-makers in achieving multi-parameter optimisation [11, 12].”