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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
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Capter 11 Filtering in Drug Discovery.

Christopher A Lipinski1

  • 1Pfizer Global Research and Development, Groton, CT 06340, USA.

Annual Reports in Computational Chemistry
|April 15, 2020
PubMed
Summary
This summary is machine-generated.

Filtering is crucial in drug discovery for optimizing compound properties and ensuring quality. This involves using drug-likeness filters, multiparameter optimization, and fingerprint algorithms to guide library design and synthesis planning.

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Area of Science:

  • Medicinal Chemistry
  • Drug Discovery
  • Computational Chemistry

Background:

  • Drug discovery involves complex filtering strategies to identify promising drug candidates.
  • Compound properties must be optimized for screening and library design.
  • Physicochemical differences exist between oral and injectable drugs.

Purpose of the Study:

  • To discuss the concept and application of filtering in drug discovery.
  • To highlight the role of filters in defining drug-likeness and optimizing compound properties.
  • To explore methods for predicting blood-brain barrier permeability and p-glycoprotein affinity.

Main Methods:

  • Incorporation of multiple filters into drug-likeness definitions.
  • Application of multiparameter optimization schemes in library design.
  • Utilizing fingerprint algorithms for diversity guidance.
  • Employing filters in chemistry synthesis planning.
  • Analyzing property differences between oral and injectable drugs.
  • Developing a scheme to separate CNS-active from non-CNS-active drugs.

Main Results:

  • Filtering leads to tradeoffs in compound properties for screening.
  • Optimization of compound properties may necessitate multiparameter optimization.
  • Fingerprint algorithms can guide diversity in compound libraries.
  • Filters ensure the synthesis of high-quality compounds.
  • Oral drugs generally exhibit lower molecular weight (MWT) and fewer hydrogen bond donors, acceptors, and rotatable bonds compared to injectable drugs.
  • A scheme was developed to predict passive blood-brain barrier (BBB) permeability and p-glycoprotein (PGP) affinity, aiding in CNS drug discovery.

Conclusions:

  • Filtering is integral to various stages of drug discovery, from library design to synthesis.
  • Understanding property differences and employing predictive models enhances the discovery of CNS-penetrant drugs.
  • P-glycoprotein (PGP) efflux ratios serve as a measure of compound affinity to PGP, a key factor in CNS drug delivery.