Most drugs exert their biological effects by binding to specific macromolecules in the body, most commonly proteins such as receptors, enzymes, or ion channels. This binding triggers a biochemical or physiological response.
A receptor is a protein molecule (usually on a cell surface or inside a cell) that recognises and binds specific chemical messengers (ligands). When a drug binds to a receptor, it can either activate or block the receptor's normal function.
| Term | Definition |
|---|
| Agonist | A drug that binds to a receptor and activates it, mimicking the natural ligand's effect |
| Antagonist | A drug that binds to a receptor but does not activate it; it blocks the receptor, preventing the natural ligand from binding |
Drug-receptor binding follows the Lock and Key model:
- The receptor (lock) has a specific active site with a defined 3D shape and chemical environment.
- The drug molecule (key) must have a complementary shape and appropriate functional groups to fit precisely into the active site.
- This explains the specificity of drug action — a drug only affects the receptor it is designed to fit.
The binding between a drug and its receptor involves several types of non-covalent interactions:
- Hydrogen bonding — between H-bond donors/acceptors on the drug and receptor
- Ionic (electrostatic) interactions — between oppositely charged groups
- Van der Waals forces — weak attractions due to temporary dipoles
- Hydrophobic interactions — non-polar regions of the drug associate with non-polar regions of the receptor
Most drug-receptor interactions are reversible because they rely on non-covalent bonds, allowing the drug to eventually dissociate and the effect to wear off.
Traditionally, discovering new drugs required years of laboratory testing of thousands of compounds. Artificial Intelligence (AI), particularly machine learning and deep learning, has revolutionised this process.
- AI models are trained on large datasets of known molecules and their biological activities.
- The model learns patterns linking molecular structure to pharmacological effect.
- It can then screen millions of virtual compounds in a fraction of the time required by traditional methods.
- AI can predict which molecules are likely to bind to a target receptor, be non-toxic, and have suitable pharmacokinetic properties.
Halicin is a landmark example of AI-assisted drug discovery:
- In 2020, researchers at MIT used a deep learning AI model to screen over 100 million chemical compounds for antibiotic activity.
- The AI identified Halicin (originally developed as a diabetes drug) as a potent antibiotic.
- Halicin showed activity against drug-resistant bacteria, including Mycobacterium tuberculosis and Clostridioides difficile.
- Its mechanism of action — disrupting the electrochemical gradient across bacterial cell membranes — is different from existing antibiotics, making resistance less likely.
- This discovery demonstrated that AI can identify novel organic molecules with medicinal potential that human researchers might overlook.
| Advantage | Detail |
|---|
| Speed | Screens millions of compounds in days rather than years |
| Cost | Reduces expensive laboratory synthesis of inactive compounds |
| Novel structures | Identifies molecules outside traditional chemical intuition |
| Multi-parameter optimisation | Simultaneously optimises efficacy, safety, and bioavailability |