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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence its...
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Local Anesthetics: Chemistry and Structure-Activity Relationship01:30

Local Anesthetics: Chemistry and Structure-Activity Relationship

Local anesthetics (LAs) are drugs that induce a temporary loss of sensation in a limited body area, preventing pain. Cocaine was the first local anesthetic discovered in the late 19th century. Cocaine is a benzoic acid ester obtained from the leaves of coca shrubs and was often used for its psychotropic effects. Cocaine was first isolated in 1860 by Albert Niemann. Sigmund Freud studied the physiological actions of cocaine. Carl Koller later introduced it into clinical practice in 1884 as a...
Adrenergic Agonists: Chemistry and Structure-Activity Relationship01:16

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

Adrenergic agonists' structure-activity relationship (SAR) determines their selectivity and efficacy. These agonists comprise a phenylethylamine moiety with an aromatic ring and an ethylamine side chain.
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of the aromatic...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:29

Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship

Indirect-acting cholinergic agonists are agents that interact with the acetylcholinesterase enzyme in the synaptic cleft, preventing the breakdown of acetylcholine into choline and acetate. Consequently, the concentration of acetylcholine in the synaptic cleft increases. These agonists can be classified into reversible and irreversible inhibitors based on their duration of action.
Reversible inhibitors display short to medium durations of action. Short-acting agents include simple alcohols with...

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Hidden active information in a random compound library: extraction using a pseudo-structure-activity relationship

Hiroaki Fukunishi1, Reiji Teramoto, Jiro Shimada

  • 1Nano Electronics Research Laboratories, Central Research Laboratories, NEC Corporation, 34, Miyukigaoka, Tsukuba, Ibaraki 305-8501, Japan. h-fukunishi@bu.jp.nec.com

Journal of Chemical Information and Modeling
|February 19, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a pseudo-structure-activity relationship (PSAR) model using high-ranked compounds from docking simulations. The PSAR model effectively identifies known active compounds, demonstrating its utility for drug discovery when known ligands are unavailable.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Machine learning in drug discovery

Background:

  • Traditional quantitative structure-activity relationship (QSAR) models require known active compounds (ligands) for training.
  • Identifying novel drug candidates often involves screening large compound libraries.
  • Docking simulations can rank compounds, but high-ranked compounds may not always be true actives.

Purpose of the Study:

  • To propose and validate a novel modeling approach, the pseudo-structure-activity relationship (PSAR) model.
  • To demonstrate that integrating information from high-ranked compounds in docking simulations can predict active compounds.
  • To show the utility of PSAR models when no known ligands are available for training.

Main Methods:

  • Development of a pseudo-structure-activity relationship (PSAR) model using pseudo-active compounds identified from docking simulations.
  • Application of the Random Forest machine learning algorithm for model construction.
  • Testing PSAR models against four diverse biological targets: estrogen receptor antagonist (ER), thymidine kinase (TK), thrombin, and acetylcholine esterase (AChE).
  • Evaluation using five different scoring functions to assess model performance.

Main Results:

  • PSAR models significantly increased the percentage of known ligands found compared to random sampling, supporting the core hypothesis.
  • PSAR models outperformed standard scoring functions in identifying known ligands, highlighting their practical applicability.
  • The PSAR model demonstrated capability in assessing compounds that initially failed docking simulations.

Conclusions:

  • The PSAR model effectively utilizes high-ranked, potentially pseudo-active compounds from docking simulations to build predictive models.
  • PSAR models offer a valuable alternative to QSAR when known ligands are scarce or novel compound types are sought.
  • This approach enhances drug discovery by improving the identification of active compounds from virtual screening efforts.