CADD, so Computer-Aided Drug Design - definition(s)

Computer-aided drug design (CADD) techniques are used to guide and speed up the early-stage development of new active compounds.

The goal is to make the clinical stage more effective (also cost-wise) by reducing the potential set for testing.

CADD entails many computational methodologies like virtual screening, virtual library design, lead optimization, de novo design, and many more. Let's focus on the first one.

Virtual screening

The term virtual screening (VS) was put in use in the late 1990s. It applies to the technique of using computational algorithms and models for the identification of potential lead molecules. It can be accomplished through scanning vast collections of chemical databases.

You can approach virtual screening in two different ways:

  • ligand-based - focusing on the ligand's properties,
  • structure-based - prioritizing the protein and filtering the database in this respect.

Because of the proven ability of computer-aided techniques to boost the selection of specific hit compounds, computational chemistry and chemoinformatics incessantly are scientific disciplines in full swing.

Why do we need perfect protein-ligand complexes?

We already know that CADD helps reduce vast chemical bases to the most promising compounds. But what is the purpose of this very strict selection?

Proteins, ligands, and the lead compound

The target is a “patient” protein and ligand is any molecule (it can be whatever, also another protein) that binds to it.

When ligand shows additional features, such as:

  • shows promise as a therapeutic for disease,
  • has the potential to lead to the development of a new drug.

we call it a lead compound.

The lead compound (sometimes just called a lead) may act on specific genes or proteins involved in a disease.

So that explains why finding the best protein-ligand pair matters. By throwing at target our well-selected ligands, we may actually cure the target protein. It's simply a clue that we may be on the trail of a new, effective drug.

CADD on the trail of leads

The more and more widespread application of diverse computer-aided methods in drug discovery has helped to better handle data associated with a large number of compounds screened against the target molecules or proteins for leads.

The CADD approach has played a crucial role in the search and optimization of potential lead compounds, also helping to save time and costs. It is widely used at various stages in drug discovery: target identification, validation, molecular design, and interactions of drug candidates with researched targets.

But let's ask a seemingly silly question. What makes CADD so much faster in selecting leads than a traditional trial and error? Apparently, some juicy abbreviations have their merits in this matter.


Quantitative structure-activity relationship (QSAR) analysis has significantly speeded up the lead optimization process in the last twenty years.

QSAR is simply a method for building mathematical models, which attempts to discover connections between the compound’s structural properties and its biological activity. So for QSAR, we need a protein's activity and the specific property, a physicochemical or structural one.

What is essential, one depends on the other, so there is a strong correlation between structure and observed properties (activity) of the compound.

That is why QSAR is often used in drug discovery to identify chemical structures that could have sound inhibitory effects on specific protein targets and have low toxicity, so fewer side effects as a drug. And from here, we can go straight to… ADMET.


ADMET stands for Absorption, Distribution, Metabolism, Excretion, and Toxicity. So the disposition of a therapeutic agent in the organism and its interactions with body chemistry and physiology. That is where a lot of potentially good leads don't make it to the tests. Also, around 60% of all drugs during clinical trials have been reported to fail at ADMET. And that's why this analysis is necessary to design a safe and effective medicine.

Just another criterion that, when applied, allows you to find the one and only... relatively small library of potential drugs. Formerly carried out at the end of the drug research process, ADMET-focused selection can now be performed through CADD methods.

Promising future and thrilling present

We hope you now find the CADD methods much more understandable. And that you know how cool they are! Because as computing power and the quality of algorithms exponentially increase, the impact of computer-aided drug design (CADD) in fighting disease skyrockets.

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