So Nobody Believes in Docking Anymore? Here's Why You Still Should!

It did not take long to decide what the first post of my new blog should be about. If you think about it, there is one method that is probably the most used (and often misused) in CADD. I am talking about molecular docking. 

(Video of molecular docking from Wikipedia)

Despite being widely used, there is a great deal of mistrust in what docking can do.

Ask around in 2025 and you’ll hear a familiar refrain: “Docking doesn’t work”, "It’s too rigid.", "The scoring functions are wrong", "Machine learning is better". And in many ways, the critics aren’t wrong — docking has well-known limitations, and its worst-case failures can be misleading or even dangerous in a screening context. But dismissing it entirely is a mistake.

Docking is not dead. It's just misunderstood.

Docking is fast and hypothesis-generating — not predictive chemistry

At its core, docking is a heuristic method for generating testable hypotheses. It doesn’t pretend to capture full protein flexibility or solvent effects (unless paired with more advanced protocols), but it gives you a plausible binding mode fast. This is invaluable when you're dealing with dozens of targets or thousands of ligands.

What docking excels at is ranking by rough compatibility — shape, chemistry, and energetics — and doing so with incredible speed. If you're expecting nanomolar predictions from a rigid-body pose scorer, you're using the tool wrong.

It works well when paired with human or domain expertise

In many cases, docking fails because it’s used in isolation or on poorly prepared systems. But when combined with:

• a reliable reference ligand,
• proper protonation and tautomeric states,
• a curated binding site,
• an ensemble of protein conformations,
• and filters for synthetic accessibility or toxicity,

…it becomes a powerful triaging tool. Many successful hit-to-lead campaigns start with a docking pipeline, not because docking is perfect, but because it gets you to “good enough” for the next round.

It's the front-end, not the final word

Most serious drug discovery projects today use docking as a first-pass filter before:

• molecular dynamics refinement,
• MM-GBSA/PBSA rescoring,
• free energy perturbation (FEP),
• or machine learning rescoring models.

These are all expensive in comparison. Docking lets you throw out the worst 95% before investing in the remaining 5%. If you think of docking as the funnel, not the filter, its role becomes obvious.

It’s still improving — just slowly

We’ve seen progress in ensemble docking, water-aware protocols, flexible receptor docking, and more physics-informed scoring functions. It's true that the field moves slowly compared to ML, but there's a decade of methodological depth here that new tools still rely on.

Bottom line

Docking is not a crystal ball. But used thoughtfully, with good structural data and chemical intuition, it remains one of the most cost-effective, scalable, and explainable tools we have in the early stages of drug discovery.

The real issue isn't that docking doesn’t work — it's that we ask too much of it.