Unknown

Dataset Information

0

AutoGrow: a novel algorithm for protein inhibitor design.


ABSTRACT: 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.

SUBMITTER: Durrant JD 

PROVIDER: S-EPMC2724963 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

altmetric image

Publications

AutoGrow: a novel algorithm for protein inhibitor design.

Durrant Jacob D JD   Amaro Rommie E RE   McCammon J Andrew JA  

Chemical biology & drug design 20090201 2


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 t  ...[more]

Similar Datasets

| S-EPMC3842281 | biostudies-literature
| S-EPMC9719503 | biostudies-literature
| S-EPMC2718647 | biostudies-literature
| S-EPMC4904165 | biostudies-literature
| S-EPMC6129278 | biostudies-literature
| S-EPMC4058937 | biostudies-literature
| S-EPMC8101011 | biostudies-literature
| S-EPMC10550942 | biostudies-literature
| S-EPMC3168723 | biostudies-literature
| S-EPMC7886686 | biostudies-literature