Searching Fragment Spaces with Feature Trees

By U. Lessel, B. Wellenzohn, Markus Lilienthal, Holger Claussen. 2009

80 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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. Gisbert Schneider
2. Francesca Grisoni
3. Nils Weskamp
4. José Jiménez-Luna
5. Markus Hartenfeller

In Computational Chemistry & Drug Discovery

1. Gisbert Schneider
2. Nils Weskamp
3. José Jiménez-Luna
4. C. K. Surratt
5. Markus Hartenfeller
6. Jeffry D. Madura
7. Francesca Grisoni
8. Michael J. Wasko

Citations in the corpus

• A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets (2015, background): “The Feature Trees (FTrees) module explores chemical spaces with fragment hopping and using overlapping fragments to create a composite ligand structure (58).”
• Enabling future drug discovery by de novo design (2011, methodology): “Two examples of software tools that make use of additive scoring schemes are BI CLAIM 116 and a computer program proposed by Nikitin et al. 61 However, regardless of these advantages, it must be stated that additivity of ligand scores is a feature that is ar-tificially introduced as it represents a s”
• Artificial intelligence in drug discovery: recent advances and future perspectives (2021, background): “These approaches are useful, for instance, to design synthetically accessible libraries [117,118], such as BI CLAIM [119] and CHIPMUNK [120].”