AutoGrow: A Novel Algorithm for Protein Inhibitor Design
By Jacob D. Durrant, R. Amaro, James Andrew McCammon. 2009
112 citations recorded by the source corpus.
Due in part to the increasing availability of crystallographic protein structures as well as rapid improvements in computing power, the past few decades have seen an explosion in the field of computer‐based rational drug design. Several algorithms have been developed to identify or generate potential ligands in silico by optimizing the ligand–receptor hydrogen bond, electrostatic, and hydrophobic interactions. We here present AutoGrow, a novel computer‐aided drug design algorithm that combines the strengths of both fragment‐based growing and docking algorithms. To validate AutoGrow, we recreate three crystallographically resolved ligands from their constituent fragments.
Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology, 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 Computational Theory and Mathematics
In Molecular Biology
In Pharmacology
In Computational Chemistry & Drug Discovery
Citations in the corpus
- A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets (2015, methodology): “AutoGrow is another software package that incorporates medicinal chemistry knowledge into ligand design (54).”