Graduate
  1. Physical Chemistry  1.1. Thermodynamics  1.1.1. Laws of Thermodynamics  1.1.2. Enthalpy and heat capacity  1.1.3. Entropy and free energy  1.1.4. Phase Equilibrium  1.1.5. Statistical thermodynamics  1.1.6. Thermodynamic cycle  1.1.7. Fugacity and activity  1.2. Quantum Chemistry  1.2.1. Principles of quantum mechanics  1.2.2. Schrödinger Equation  1.2.3. Particle in a box  1.2.4. Operators and eigenvalues  1.2.5. Atomic orbitals  1.2.6. Molecular orbital theory  1.2.7. Perturbation theory  1.2.8. Valence bond theory  1.2.9. Hybridization and chemical bonding  1.3. Chemical kinetics  1.3.1. Rate laws and reaction mechanisms  1.3.2. Collision theory  1.3.3. Transition state theory  1.3.4. Enzyme Kinetics  1.3.5. Chain Reactions and Polymerization  1.3.6. Photochemical reactions  1.4. Statistical mechanics  1.4.1. Partitioning functions  1.4.2. Molecular distribution functions  1.4.3. Boltzmann Distribution  1.4.4. Bose–Einstein and Fermi–Dirac statistics  1.5. Spectroscopy  1.5.1. Rotational Spectroscopy  1.5.2. Vibrational Spectroscopy  1.5.3. Electronic Spectroscopy  1.5.4. Nuclear magnetic resonance spectroscopy  1.5.5. Mass Spectrometry  1.5.6. Raman Spectroscopy  1.5.7. Electron paramagnetic resonance spectroscopy  1.6. Surface and colloidal chemistry  1.6.1. Absorption isotherm  1.6.2. Surface tension and wetting  1.6.3. Colloidal Stability  1.6.4. Catalysis  1.6.5. Emulsions and micelles  1.7. Electrochemistry  1.7.1. Nernst equation  1.7.2. Electrochemical cells  1.7.3. Conductivity and mobility  1.7.4. War  1.7.5. Fuel cells  1.7.6. Electrolysis
  2. Organic chemistry  2.1. Reaction mechanism  2.1.1. Nucleophilic substitution reactions  2.1.2. Electrophilic addition reactions  2.1.3. Elimination reactions  2.1.4. Rearrangement reactions  2.1.5. Radical reactions  2.1.6. Pericyclic reactions  2.2. Spectroscopy and structural determination  2.2.1. UV-Vis Spectroscopy  2.2.2. IR Spectroscopy  2.2.3. NMR Spectroscopy  2.2.4. Mass Spectrometry  2.2.5. X-ray crystallography  2.3. Stereoscopic  2.3.1. Chirality and optical activity  2.3.2. Structural analysis  2.3.3. Geometrical isomerism  2.3.4. Dynamic Stereochemistry  2.4. Organometallic Chemistry  2.4.1. Organolithium and organomagnesium reagents  2.4.2. Palladium-catalyzed cross-coupling reactions  2.4.3. Transition Metal Complexes  2.4.4. Metal–carbon bond  2.5. Polymer chemistry  2.5.1. Polymerization mechanism  2.5.2. Polymer Characteristics  2.5.3. Biodegradable Polymer  2.5.4. Conducting polymer  2.5.5. Supramolecular polymers  2.6. Medicinal chemistry  2.6.1. Drug design and development  2.6.2. Pharmacokinetics and pharmacodynamics  2.6.3. Structure-activity relationships  2.6.4. Molecular docking and drug screening
  3. Inorganic chemistry  3.1. Coordination chemistry  3.1.1. Crystal field theory  3.1.2. Ligand field theory  3.1.3. Spectrochemical Series  3.1.4. Chelation and stability  3.2. Organometallic Chemistry  3.2.1. Metal Carbonyls  3.2.2. Catalysis by organometallic complexes  3.2.3. Metallocenes  3.3. Bio-inorganic chemistry  3.3.1. Metalloproteins and enzymes  3.3.2. The role of metals in biological systems  3.3.3. Metal ion transport and storage  3.4. Solid state chemistry  3.4.1. Crystal Structures  3.4.2. Band theory of solids  3.4.3. Superconductors  3.4.4. Defects in the crystal  3.5. Lanthanides and Actinides  3.5.1. Electronic configuration in lanthanides and actinides  3.5.2. Coordination chemistry of the lanthanides  3.5.3. Magnetic Properties of Lanthanides
  4. Analytical chemistry  4.1. Chromatography  4.1.1. Gas Chromatography  4.1.2. High Performance Liquid Chromatography  4.1.3. Thin Layer Chromatography  4.2. Spectroscopic Techniques  4.2.1. Atomic absorption spectroscopy  4.2.2. X-ray diffraction  4.2.3. Inductively coupled plasma spectroscopy  4.3. Electroanalytical methods  4.3.1. Potentiometry  4.3.2. Voltammetry  4.3.3. Colometry  4.4. Mass Spectrometry  4.4.1. Ionization Technique  4.4.2. Fragmentation Pattern  4.5. Chemometrics  4.5.1. Multidisciplinary analysis  4.5.2. Machine Learning in Chemistry
  5. Theoretical and Computational Chemistry  5.1. Molecular dynamics simulation  5.1.1. Force fields and energy minimization  5.1.2. Monte Carlo simulation in molecular dynamics simulations  5.2. Quantum chemical methods  5.2.1. Hartree–Fock theory  5.2.2. Density functional theory  5.2.3. Semi-empirical methods  5.3. Computational drug design  5.3.1. Molecular Docking  5.3.2. QSAR Modeling  5.3.3. Virtual Screening
  6. Biochemistry  6.1. Enzyme Kinetics  6.1.1. Michaelis-Menten kinetics  6.1.2. Inhibition mechanism  6.2. Metabolism and Bioenergetics  6.2.1. Glycolysis  6.2.2. Citric acid cycle  6.2.3. Electron transport chain  6.3. molecular Biology  6.3.1. DNA replication and repair  6.3.2. Protein synthesis  6.3.3. Gene Regulation  6.4. Structural Biochemistry  6.4.1. Protein Folding  6.4.2. Membrane Biophysics
  7. Environmental Chemistry  7.1. Atmospheric Chemistry  7.1.1. Greenhouse gases  7.1.2. Ozone depletion  7.1.3. Air pollutants and smoke  7.2. Water Chemistry  7.2.1. pH and water hardness  7.2.2. Wastewater treatment  7.2.3. Aquatic Chemistry  7.3. Soil Chemistry  7.3.1. Heavy metal contamination  7.3.2. Soil pH and Buffering  7.3.3. Nutrient cycling  7.4. Toxicology and Chemical Safety  7.4.1. Toxicokinetics  7.4.2. Risk Assessment in Toxicology and Chemical Safety in Environmental Chemistry  7.4.3. Effects of pollutants on the environment

Graduate → Organic chemistry → Medicinal chemistry ↓


Molecular docking and drug screening


Introduction

Molecular docking and drug screening are essential processes in medicinal and organic chemistry. They help understand how potential drugs interact with their targets, such as proteins, enzymes or DNA, at the molecular level. These methods are crucial for identifying new drugs and designing molecules that can fit precisely into specific locations on a target, much like a key fits into a lock.

Understanding molecular docking

Molecular docking is the study of how two or more molecular structures fit against each other. Imagine a glove (the receptor) and a hand (the ligand). Just as you need your hand to fit perfectly inside the glove, the molecules need to fit perfectly with each other to initiate their action.

Docking process

The docking process involves several steps. First, we have to identify the active site of the receptor, where the interaction with the ligand will take place. Then, the structure of the ligand is optimized in such a way that it can fit into the active site of the receptor.

Receptor Ligand

Once the molecular structure is drawn, the next step is to position the ligand within the receptor's active site. Various software tools can simulate the interactions and suggest the optimal configuration using scoring functions.

Scoring function

Scoring functions play an important role in evaluating how well a ligand can fit into the receptor. They calculate the binding affinity or strength of interaction between the ligand and the receptor.

Some common types of scoring functions are:

  • Energy-based scoring: calculates the total energy of the system, with the aim of finding the configuration with the lowest energy state.
  • Knowledge-based scoring: using statistical data from known complex structures to predict how new ligands will behave.
  • Empirical scoring: takes into account various features such as hydrogen bonding, steric effects, and solvation energy.
Energy Level Optimal composition

Role in drug screening

Drug screening is a crucial stage in drug discovery, where compounds are tested for their effectiveness as drugs. Molecular docking is a tool often used during this stage to predict interactions between drugs and their targets.

Virtual screening

Virtual screening is a technique that uses computational methods to search large databases of chemical compounds. By using molecular docking, potential drugs can be rapidly evaluated.

This process involves the following steps:

  1. Selection of target molecules for potential drug interactions.
  2. Preparation of both target and ligand libraries with potential activators or inhibitors.
  3. Docking simulations between the target and each ligand to predict binding affinities.
  4. Analysis of results and selection of promising candidates for further testing.
Compound Library Docking Results

Fundamentals of chemistry

Understanding the chemistry behind molecular docking and drug screening involves several fundamental concepts. Interactions such as hydrogen bonding, van der Waals forces, and the hydrophobic effect are important here.

Chemical interaction

Hydrogen bond: It is a strong type of dipole-dipole attraction between an electronegative atom in a compound and a hydrogen atom bonded to another electronegative atom.

H – O – H ... :O = C

water carbonyl

Van der Waals forces: These are weak forces acting between molecules due to the dipole moment caused by the electron cloud around an atom or molecule.

Hydrophobic effect: The tendency of nonpolar substances to cluster together in aqueous solution, thereby decreasing their interaction with water.

Static views

Steric effects refer to the effect of the physical size and shape of molecules on their reactivity and interactions. Large substituents or groups can hinder the accessibility of certain sites.

During docking, steric clashes are important, as they can prevent potential drugs from fitting properly into the binding site.

Challenges and opportunities

Although molecular docking and drug screening offer remarkable capabilities for drug discovery, they also bring challenges as well as opportunities. Predicting the exact binding mode and accurately ranking compounds can sometimes be difficult.

Challenges

Flexibility of molecules: Both ligands and receptors can have considerable flexibility, making it difficult to predict the correct conformation during binding.

Water molecules: The role of water in the binding process is not fully understood and can affect the docking results.

Opportunity

Advances in computer processing power and algorithms are making molecular docking more accurate and faster. New scoring functions and models continue to improve prediction capabilities.

A better understanding of the biological context and integration with other experimental data could significantly enhance predictive modeling.


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Molecular docking and drug screening

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