1. Discovery and Preclinical Research
Potential drugs are identified and tested in non-human studies

2. Clinical Trials
Testing in human subjects to assess safety and efficacy

3. Regulatory Approval
Evaluation by agencies like the FDA before the drug can be marketed

4. Post-Marketing Surveillance:
Ongoing monitoring after the drug is available to the public

Computation is most helpful with the drug discovery stage

Proteins regulate nearly all cellular processes and drugs can inhibit or activate proteins to correct disease states

Criteria for Selecting a Protein Target

• Disease Relevance: The protein plays a critical role in the disease mechanism.
• Druggability: The target has a structure that allows it to bind with drug-like molecules.
• Specificity: Targeting the protein minimizes effects on healthy cells, reducing side effects.

Example: Bruton’s tyrosine kinase (BTK) is a critical signaling enzyme that controls B-cell development, maturation, and activation by mediating B-cell receptor signal transduction

(Mohamed et al., 2009)

Effective drugs must bind to the target protein with sufficient affinity and specificity

Estimated to be between 10⁶⁰ to 10²⁰⁰ possible small organic molecules

We need methods to navigate chemical space and identify promising leads accurately and efficiently

Experimental assays are still expensive, and limited to commercially available compounds

Instead, we can use computational methods to predict which compounds we should experimental validate

Can screen millions to billions of compounds in silico, thereby dramatically expanding our search space

Binding occurs when a compound/ligand interacts specifically with a protein

We can model this as a reversible protein-ligand binding

Entropy

Enthalpy

Accounts for energetic interactions

How much conformational flexibility changes

Note: Simulations capture free energy directly instead of treating enthalpy and entropy separately

Noncovalent interactions: Electrostatics, hydrogen bonds, dipoles, π-π stacking, etc.

Ensemble differences in noncovalent interactions provide binding enthalpy

Ensemble average

Our noncovalent interactions conceptual framework:

3. Regions of increased electron density are associated with higher partial negative charges

4. Electrons are mobile and can be perturbed by external interactions

1. Coulomb's law describes the interactions between charges

Molecular interactions are governed by their electron densities (Hohenberg-Kohn theorem)

This is rather difficult, so we often use conceptual frameworks to explain trends (e.g., hybridization and resonance)

2. Molecular geometry uniquely specifies an electron density

Charged molecules have a net imbalance between

• Positive charges in their nuclei
• Negative charges from their electrons

This leads to net electrostatic attractions or repulsions between different atoms or molecules

Arginine

Glycine

~5 to 20 kcal/mol per interaction

Attraction between a (donor) hydrogen atom covalently bonded to an electronegative atom and another (acceptor) electronegative atom with a lone pair

• Common donors: O-H, N-H groups
• Common acceptors: O and N atoms with lone pairs

~2 to 7 kcal/mol per hydrogen bond

Strongest when the hydrogen, donor, and acceptor atoms are colinear

Electronegativity differences lead to unequal distribution of electron density

Unequal distribution results in regions or partial positive or partial negative charges

Consistent electron density spatial variation results in permanent dipoles

~0.01 to 1 kcal/mol per interaction

Dispersion: Electrons in molecules are constantly moving, leading to temporary uneven distributions that induce dipoles in neighboring molecules

~0.4 to 4 kcal/mol per interaction

Induction: The electric field of a polar molecule distorts the electron cloud of a nonpolar molecule, creating a temporary dipole

Noncovalent interactions between aromatic rings due to overlap of π-electron clouds

~1 to 15 kcal/mol per interaction

Edge-to-face

Displaced

Face-to-face

One of Alex's esoteric points: "Entropy is disorder," is a massive oversimplification that breaks down in actual practice

Entropy is formally defined as 

is the total number of microstates available to the system without changing the system state

Entropy is "energy dispersion"

Higher entropy implies greater microstate diversity

"System state" can be arbitrarily defined and compared as

• Unbound ligand vs. bound ligand
• Unfolded protein vs. folded protein
• Liquid water at 300 K vs. 500 K

Suppose I have a system with

• Protein receptor
• Ligands positioned on a grid

My macrostate (number of particles, temperature, and pressure) remain constant

How many ways can I rearrange the ligands without binding to the receptor?

Number of ligands

Number of sites

Number of ways to choose L grid sites out of N is the binomial coefficient

What if one ligand binds to the receptor?

How does entropy change?

Increase

No change

Decrease

It depends on our ligand concentration!

How to interpret this: Pick a number of ligands and move to the right (L - 1), does entropy go up or down?

To compute the free energy of a "system state", we have to compute the state's partition function, Z

We can run simulations and directly compute the ensemble average free energy

This is theoretically valid but not practical. Why?

To compute the partition function of protein; for example, we need to know the energy for

This is impossible

Fortunately, the low-energy conformations contribute the most to the partition function

Molecular simulations can sample some low-energy conformations; however, minor errors will drastically impact absolute free energy calculation

The free energy change from state A to B can be computed as

Advantage: Partition function ratios are dominated by overlapping microstates common between states A and B

Maintaining phase space overlap ensures more reliable and converged free energy estimates

(This is conceptually similar to having a small integration step size.)