PHD
  1. Inorganic chemistry  1.1. Coordination chemistry  1.1.1. Ligand field theory  1.1.2. Crystal field theory  1.1.3. Molecular Orbital Theory for Coordination Compounds  1.1.4. Stability of Coordination Compounds  1.1.5. Electronic spectra of coordination compounds  1.1.6. Isomerism in Coordination Compounds  1.1.7. Kinetics and mechanism of coordination reactions  1.2. Organometallic Chemistry  1.2.1. Metal Carbonyls  1.2.2. Metal alkyl and aryl  1.2.3. Fischer and Schrock carbonaceans  1.2.4. Oxidative Addition and Reductive Elimination  1.2.5. Metal Hydrides  1.2.6. Catalysis by organometallic compounds  1.2.7. Metallocenes and Sandwich Complexes  1.2.8. CH activation  1.3. Solid state chemistry  1.3.1. Crystal structures and lattices  1.3.2. Band theory and electrical properties  1.3.3. Defects in the crystal  1.3.4. Synthesis of solid state materials  1.3.5. Magnetic and optical properties of solids  1.3.6. Superconductivity  1.3.7. Solid state ionics  1.4. Bio-inorganic chemistry  1.4.1. Metalloproteins and enzymes  1.4.2. Metal ion transport and storage  1.4.3. The role of metals in medicine  1.4.4. Bioinspired catalysis  1.4.5. Metal–DNA interactions  1.4.6. Metal Complexes in Medical Science  1.5. Lanthanides and Actinides  1.5.1. Electronic configuration and oxidation states in lanthanides and actinides  1.5.2. Spectral and Magnetic Properties in Lanthanides and Actinides  1.5.3. Separation and Extraction of Lanthanides and Actinides  1.5.4. Coordination chemistry of f-block elements  1.6. Main Group Chemistry  1.6.1. Chemistry of boron compounds  1.6.2. Chemistry of Silicon and Germanium Compounds  1.6.3. Chemistry of Phosphorus and Sulfur Compounds  1.6.4. Organosilicon chemistry  1.6.5. Noble Gas Chemistry
  2. Organic chemistry  2.1. Reaction mechanism  2.1.1. Nucleophilic substitution reactions  2.1.2. Electrophilic addition and substitution reactions  2.1.3. Elimination reactions  2.1.4. Rearrangement reactions  2.1.5. Radical reactions  2.1.6. Pericyclic Reactions  2.1.7. Photochemical reactions  2.2. Stereoscopic  2.2.1. Chirality and optical activity  2.2.2. Structural analysis  2.2.3. Stereoelectronic effects  2.2.4. Asymmetric synthesis  2.2.5. Dynamic Stereochemistry  2.3. Organometallic Chemistry in Organic Synthesis  2.3.1. Organolithium and organomagnesium reagents  2.3.2. Transition metal catalyzed coupling reactions  2.3.3. Palladium-catalyzed reactions  2.3.4. Olefin Metathesis  2.3.5. Gold and silver catalysis  2.4. Natural products chemistry  2.4.1. Alkaloids and terpenoids  2.4.2. Steroids and Flavonoids  2.4.3. Total synthesis of natural products  2.4.4. Biosynthesis of natural products  2.5. Supramolecular Chemistry  2.5.1. Host-Guest Chemistry  2.5.2. Self-assembly and molecular recognition  2.5.3. Supramolecular Catalysis  2.5.4. Molecular Machines
  3. Physical Chemistry  3.1. Thermodynamics  3.1.1. Laws of Thermodynamics  3.1.2. Chemical Potential and Equilibrium  3.1.3. Statistical thermodynamics  3.1.4. Non-equilibrium thermodynamics  3.2. Quantum Chemistry  3.2.1. Schrödinger equation and its applications  3.2.2. Molecular orbital theory  3.2.3. Density functional theory  3.2.4. Post-Hartree–Fock methods  3.3. Chemical kinetics  3.3.1. Reaction rate theory  3.3.2. Mechanism and rate laws  3.3.3. Catalysis and enzyme kinetics  3.3.4. Reaction dynamics  3.4. Spectroscopy and molecular structure  3.4.1. Infrared Spectroscopy  3.4.2. UV-visible spectroscopy  3.4.3. Nuclear Magnetic Resonance Spectroscopy  3.4.4. Mass Spectrometry  3.4.5. Raman Spectroscopy  3.5. Surface and Colloid Chemistry  3.5.1. Absorption and Catalysis  3.5.2. Surfactants and micelles  3.5.3. Colloidal Stability  3.5.4. Nanomaterials and Interfaces
  4. Analytical chemistry  4.1. Chromatography  4.1.1. Gas Chromatography  4.1.2. High Performance Liquid Chromatography  4.1.3. Thin Layer Chromatography  4.1.4. Ion Chromatography  4.2. Electroanalytical techniques  4.2.1. Voltammetry and Polarography  4.2.2. Conductometry and potentiometry  4.2.3. Colometry  4.3. Spectroscopic Methods  4.3.1. Atomic absorption spectroscopy  4.3.2. Fluorescence Spectroscopy  4.3.3. X-ray diffraction  4.3.4. Electron paramagnetic resonance spectroscopy  4.4. Chemometrics  4.4.1. Data processing and statistical methods  4.4.2. Multidisciplinary analysis  4.4.3. Machine Learning in Analytical Chemistry
  5. Theoretical and Computational Chemistry  5.1. Molecular dynamics simulation  5.2. Quantum chemistry methods  5.3. Computational drug design  5.4. Machine Learning in Chemistry  5.5. Ab initio molecular dynamics
  6. Biophysics and Medicinal Chemistry  6.1. Drug discovery and design  6.2. Enzyme kinetics and inhibition  6.3. Chemical biology approach  6.4. Protein-ligand interactions  6.5. Bioorthogonal chemistry
  7. Materials chemistry  7.1. Polymer chemistry  7.1.1. Polymerization mechanism  7.1.2. Functional Polymers  7.1.3. Biodegradable Polymer  7.1.4. Conducting and semiconducting polymers  7.2. Nano Chemistry  7.2.1. Nanoparticles and nanostructures  7.2.2. Carbon-based nanomaterials  7.2.3. Quantum dots  7.3. Energy and Environmental Chemistry  7.3.1. Battery and Fuel Cell Chemistry  7.3.2. Green chemistry and sustainable materials  7.3.3. Photocatalysis

PHD → Theoretical and Computational Chemistry ↓


Computational drug design


Computational drug design is an essential component of theoretical and computational chemistry that deals with creating and optimizing new pharmaceutical compounds using computer-based techniques. This innovative approach combines principles of chemistry, biology, and computer science to accelerate the drug development process, significantly reducing both time and financial costs compared to traditional experimental methods. Let's look at this fascinating field in detail, guided by a structured explanation.

Introduction to drug design

Before delving into computational drug design, it is important to understand what drug design is. Drug design involves discovering new potential drugs based on knowledge of biological targets, usually proteins involved in disease pathways.

Target (Protein) ➜ Disease Pathway ➜ Drug Design

In brief, the goal of drug design is to identify small molecules or biologics that can modulate the activity of a protein target, thereby interrupting the disease process.

Role of computational techniques

Traditionally, drug design relied heavily on trial-and-error laboratory experiments. However, with the advancement of computational techniques, researchers can simulate and predict the behavior of molecules, greatly accelerating the identification of potential drug candidates.

Key methods in computational drug design

1. Molecular docking

Molecular docking is a method that models the interaction between a small molecule (ligand) and a target protein. It predicts the preferred orientation of the ligand when bound to the protein, allowing researchers to estimate binding affinity and specificity.

Protein Surface Ligand

The above figure shows a ligand (in red) docking onto the surface of a protein.

2. Quantitative structure-activity relationship (QSAR)

QSAR is a method that uses statistical models to predict the activity of new chemical compounds based on their chemical structure. It is based on the idea that similar structures have similar activity.

Chemical Structure ➜ QSAR Model ➜ Activity Prediction

3. Molecular dynamics (MD) simulations

MD simulations are used to study the physical movements of atoms and molecules over time. This method provides information about the flexibility and conformational changes of both the protein and the ligand, which are important for understanding binding interactions.

Trajectory

The blue line shows the possible trajectories of the molecule over time during the simulation.

4. Pharmacophore modeling

Pharmacophores represent essential features of a molecule required for pharmacological activity. This method identifies common patterns and features in active compounds, helping to design new molecules that retain these key features.

Active Compound ➜ Common Features ➜ Pharmacophore Model

The process of computational drug design

The computational drug design process typically consists of several major steps:

Step 1: Identify the target

The first step involves identifying the biological target, which is usually a protein associated with the disease. This step requires deep biological insight and understanding of the disease mechanism.

Step 2: Structure selection

Once the target is selected, researchers collect its structural information. High-resolution techniques such as X-ray crystallography or NMR are typically used to obtain the protein structure.

Step 3: Virtual screening

In virtual screening, a large library of compounds is screened against the target protein to identify potential ligands. Computational docking and other methods are used to evaluate binding interactions.

Step 4: Lead optimization

The lead compounds identified from virtual screening undergo optimization to enhance their binding affinity, specificity and pharmacokinetic properties.

Lead Compounds ➜ Optimization ➜ Improved Drug Candidates

Step 5: ADMET predictions

ADMET stands for absorption, distribution, metabolism, excretion, and toxicity. These are important properties for assessing the drug-likeness of a compound, which can be predicted using computational models.

Case study: Designing a drug that inhibits HIV protease

To illustrate computational drug design, consider the design of a drug that inhibits the HIV protease enzyme, which is critical for replication of the virus.

1. Identifying the HIV protease as a target

The HIV protease enzyme has been identified as a target, as it plays a vital role in the processing of viral proteins essential for virus maturation.

2. Determination of the structure of HIV protease

The three-dimensional structure of the HIV protease is determined using structural biology techniques, providing a blueprint for drug design.

3. Virtual screening against the compound library

A virtual screening campaign is conducted using molecular docking to identify compounds that can bind and inhibit the HIV protease enzyme.

4. Lead optimization

Lead compounds obtained from screening enter the optimization phase, where their structures are modified to improve potency and pharmacokinetic properties.

5. ADMET assessment

ADMET prediction is performed to evaluate the druggability of optimized compounds, thereby guiding further modifications or selections.

Advantages of computational drug design

Computational drug design offers several advantages:

  • Cost-effective: It reduces the cost of drug discovery by quickly eliminating unsuccessful candidates.
  • Time savings: Speeds up the discovery process by quickly screening large compound libraries.
  • Greater precision: Provides detailed insight into molecular interactions, enhancing selectivity and specificity.

Challenges in computational drug design

Despite its advantages, computational drug design faces challenges that need to be addressed:

  • Limitations of models: Computational models may not always accurately predict biological activity due to simplifications.
  • Data quality: Reliable structural and biological data are essential for accurate predictions.
  • Computational resources: Simulations and large-scale screening often require high computational power.

Future directions

The future of computational drug design is promising, with advancements in machine learning, cloud computing, and quantum computing set to revolutionize the field. Integration with big data analytics and artificial intelligence is expected to increase the accuracy and speed of drug discovery processes.

In short, computational drug design is a cornerstone of modern theoretical and computational chemistry. It bridges the gap between biological insights and chemical innovations, helping researchers to design new therapies efficiently and effectively. As the technology continues to develop, so will our ability to develop lifesaving medicines that address a range of health challenges around the world.


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Computational drug design

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