Enabling future drug discovery by de novo design

By Markus Hartenfeller, Gisbert Schneider. 2011

92 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Theory and Mathematics, Molecular Biology, 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 Biomedical Engineering

1. Sergey I. Nikolenko
2. Marwin H. S. Segler

In Computational Theory and Mathematics

1. Sergey I. Nikolenko
2. Zhiyong Lu
3. Brett K. Beaulieu-Jones
4. Yanjun Qi
5. Jinbo Xu
6. Casey S. Greene
7. Srinivas C. Turaga
8. Marwin H. S. Segler

In Molecular Biology

1. Casey S. Greene
2. Sergey I. Nikolenko
3. Yifan Peng
4. Brett K. Beaulieu-Jones
5. G. Way
6. Amr M. Alexandari
7. Zhiyong Lu
8. Avanti Shrikumar

In Computational Chemistry & Drug Discovery

1. Sergey I. Nikolenko
2. Jinbo Xu
3. Marwin H. S. Segler
4. S. Joshua Swamidass

Citations in the corpus

• Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Whereas the goal of virtual screening is to find active molecules by predicting the biochemical activity of hundreds of thousands to millions of chemicals using existing (virtual) chemical libraries, analogous to robotic high-throughput "wet lab" screening, de novo drug design aims to directly gener”
• Synthetic Data for Deep Learning (2019, background): “In particular, de novo drug design [235, 528] is a field that searches for molecules with desirable properties in a search space of about 10(60) synthesizable molecules [195,492], and the goal is to find (which in a space of this size rather means to generate) candidate molecules that would later ha”