PARM: A genetic evolved algorithm to predict bioactivity / J. Chem. Inf. Comput. Sci.
- Authors
- Publication Date
- Mar 01, 1998
- Source
- Institutional Repository of Institute of Process Engineering, CAS (IPE-IR）
- Keywords
- License
- Unknown
- External links
Abstract
Based on Waiters' GERM (Genetic Evolved Receptor Model) algorithm, an improved algorithm FARM (Pseudo Atomic Receptor Model) was put forward. PARM uses a combination of a genetic algorithm and a cross-validation technique to produce an atomic-level pseudoreceptor model, based on a set of known structure-activity relationships. During the genetic process, an artificial interfering method, which is based on a complementary principle of ligand-receptor interaction, was used to accelerate the search speed. The evolved models show a high correlation between intermolecular energy and bioactivity and can predict the bioactivity of an unknown molecule by interpolating in the regression equation of the structure-activity relationship. This algorithm was applied to two systems and produced reasonable results. / Based on Waiters' GERM (Genetic Evolved Receptor Model) algorithm, an improved algorithm FARM (Pseudo Atomic Receptor Model) was put forward. PARM uses a combination of a genetic algorithm and a cross-validation technique to produce an atomic-level pseudoreceptor model, based on a set of known structure-activity relationships. During the genetic process, an artificial interfering method, which is based on a complementary principle of ligand-receptor interaction, was used to accelerate the search speed. The evolved models show a high correlation between intermolecular energy and bioactivity and can predict the bioactivity of an unknown molecule by interpolating in the regression equation of the structure-activity relationship. This algorithm was applied to two systems and produced reasonable results.
