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Drug Discovery: Overview01:26

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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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The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50...
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The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
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Organic molecules primarily contain carbon and hydrogen atoms. While all the hydrogen isotopes are NMR-active, protium or hydrogen-1 is the most abundant. It has a significant energy separation between its nuclear spin states due to its large gyromagnetic ratio. As per Boltzmann's distribution, an increase in the energy separation implies a greater excess population of nuclei available for excitation, resulting in a strong NMR absorption signal.
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The position of the absorption signal of a sample is reported relative to the position of the signal of tetramethylsilane (TMS), which is added as an internal reference while recording spectra. The difference between the absorption frequencies of the sample and TMS (in Hz) is divided by the spectrometer operating frequency (in MHz) to obtain a dimensionless quantity called the chemical shift. It is reported on the δ (delta) scale and expressed in parts per million.
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The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
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Recent Updates on Computer-aided Drug Discovery: Time for a Paradigm Shift.

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Computer-Aided Drug Designing (CADD) accelerates drug discovery by reducing costs and time. This review covers CADD methods, targets, and computational tools for identifying effective drug molecules.

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CADD.Drug discoveryMolecular dynamicsPharmacophoreReceptor-ligandin-silico

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Area of Science:

  • Computational chemistry and pharmaceutical sciences.

Background:

  • Computer-Aided Drug Designing (CADD) is integral to modern drug discovery.
  • It streamlines the identification and development of novel therapeutic agents.

Purpose of the Study:

  • To provide a comprehensive review of CADD methodologies.
  • To discuss the advantages, disadvantages, and applications of various CADD approaches.
  • To highlight the role of in-silico predictions in drug development.

Main Methods:

  • Review of existing literature on CADD techniques.
  • Categorization of CADD approaches: Structure-Based Drug Designing (SBDD), Ligand-Based Drug Designing (LBDD), Pharmacophore-Based Drug Designing (PBDD), and Fragment-Based Drug Designing (FBDD).
  • Discussion of in-silico pharmacokinetic, pharmacodynamic, and toxicity predictions.

Main Results:

  • CADD significantly reduces time and cost in early-stage drug development.
  • Various CADD types offer distinct strategies for drug design.
  • In-silico filters are crucial for assessing drug-likeness and potential efficacy.
  • A wide array of computational tools and software are available for CADD.

Conclusions:

  • CADD is an indispensable tool in pharmaceutical research.
  • The integration of diverse CADD methods and in-silico predictions enhances the efficiency of drug discovery.
  • Knowledge of available computational resources is vital for successful CADD implementation.