SeleX-CS: A New Consensus Scoring Algorithm for Hit Discovery and Lead Optimization

By S. Bar-Haim, A. Aharon, Tal Ben-Moshe, Y. Marantz, H. Senderowitz. 2009

32 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, 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 Artificial Intelligence

1. Sandeepkumar Kothiwale
2. Jens Meiler
3. E. W. Lowe
4. Gregory Sliwoski

In Computational Theory and Mathematics

1. Elizabeth Yuriev
2. Sandeepkumar Kothiwale
3. Jens Meiler
4. E. W. Lowe
5. Paul A. Ramsland
6. Mark Agostino
7. Gregory Sliwoski
8. Wenbo Yu

In Computational Chemistry & Drug Discovery

1. Elizabeth Yuriev
2. Sandeepkumar Kothiwale
3. Jens Meiler
4. E. W. Lowe
5. Paul A. Ramsland
6. Mark Agostino
7. Gregory Sliwoski
8. Wenbo Yu

Citations in the corpus

• Computational Methods in Drug Discovery (2014, methodology): “0 was followed by ranking based on a rank-by-vote and rankby-number consensus scoring approach (Bar-Haim et al., 2009).”
• Challenges and advances in computational docking: 2009 in review (2011, methodology): “In SeleX-CS, a subset of scoring functions is initially allowed to form a consensus score, and that subset is consequently optimized using the Monte Carlo/Simulated Annealing procedure.”