Major Approaches in CADD

4. Major Approaches in CADD.

CADD methodologies are broadly categorized into two main approaches, which are often used in a complementary fashion: 

(i) Structure-Based Drug Design (SBDD)

Concept:

SBDD is employed when the 3D structure of the biological target is known. It's akin to designing a particular key by precisely knowing the contours of the lock.

Prerequisites:

3D Target Structure: These structures are primarily obtained through experimental techniques such as X-ray crystallography, Cryo-electron Microscopy (Cryo-EM), or Nuclear Magnetic Resonance (NMR) spectroscopy. If experimental data are unavailable, computational methods such as homology modeling can be used to predict the structure.

Ligand Database: A vast collection of potential drug molecules to be screened against the target.

Key Techniques:

(a) Molecular Docking: This is a computational simulation that predicts how a ligand (small molecule) fits into the binding pocket of a target protein.

Pose Prediction: The software predicts various possible orientations and conformations (poses) of the ligand within the binding site.

Scoring Functions: Mathematical algorithms are used to estimate the binding affinity between the ligand and the target for each pose, providing a "score" that indicates the quality of the fit and predicted binding strength.

Figure 1: Representative Structure of Molecular Docking & Molecular Dynamics Simulation of Protein and Ligand.

(b) Molecular Dynamics (MD) Simulations:  MD simulates the dynamic movement of atoms and molecules over time.

Importance: Proteins and ligands are not static; they are flexible and constantly in motion. MD simulations help us understand this dynamic behavior and how it influences ligand binding, conformational changes in the target, and overall stability of the complex.

Insights: MD provides deeper insights into the binding mechanisms, the stability of protein-ligand complexes, and the identification of transient binding pockets.

(c) De Novo Design: This advanced technique involves designing entirely new molecules from scratch, atom by atom, directly within the binding pocket of the target, optimizing for desired interactions.

(ii) Ligand-Based Drug Design (LBDD) 

Concept:

LBDD is utilized when the 3D structure of the biological target is unknown or not clearly defined. Instead, this approach relies on information derived from a set of known active molecules (ligands) that bind to the target. It's like trying to deduce the shape of a lock by studying a collection of keys that are known to open it. 

Prerequisites:

A set of molecules with experimentally validated biological activity against the target of interest.

Key Techniques:

Quantitative Structure-Activity Relationship (QSAR): QSAR involves developing mathematical models that correlate the chemical features (descriptors) of a set of molecules with their observed biological activity. Example: A QSAR model might predict that "molecules with a certain size, specific types of chemical groups, and particular electronic properties tend to exhibit higher activity."

Pharmacophore Modeling: This technique identifies and maps the essential 3D arrangement of chemical features (e.g., hydrogen bond donors, hydrogen bond acceptors, hydrophobic centers, positively or negatively charged groups) that are crucial for a molecule to bind to a target and elicit a biological response. Think of a pharmacophore as a "fingerprint" or "blueprint" of the molecular features required for activity, independent of the exact chemical structure.

Similarity Searching: This method aims to find new molecules that are chemically similar to known active compounds. The underlying principle is the "structure-activity relationship" hypothesis, which suggests that molecules with similar structures often possess similar biological activities.

Figure 2: Descriptive image of Quantitative Structure-Activity Relationship (QSAR) and Pharmacophore Modeling