From Simulated Annealing to Quantum Entropy, A Personal Snapshot of Molecular Docking: 1990 → 2026

Before diving into history, a personal note. My name is Mansour Ansari. I am a retired software engineer building something quietly extraordinary from my back office in Oklahoma. After a long journey building QuantumCURE Pro™, a quantum-enhanced cloud-native drug discovery platform, refining docking pipelines, deploying Vina workers, and experimenting with entropy sources, I arrived at a quiet realization worth sharing. I am not a chemist. I never came from academia. What I am is a software engineer with 30 years of mission-critical systems experience, including pioneering live wireless video streaming from inside tornado-chase vehicles for major broadcast networks, before streaming was even a word people used, saving lives through early warning at the edge of what was technically possible.

When I retired in 2015, I set out to find a new challenge worthy of the next chapter. What I did not expect was to find myself contributing to the field of molecular docking, without ever setting out to “join” it.

I am not a large pharmaceutical company. I am not a university lab with grant funding. I am a retired software engineer who became genuinely obsessed with a single question: how does randomness shape simulation?

What started as a single experiment, connecting a USB quantum random number generator from Cryptalabs to a docking workflow, slowly evolved into something more philosophical:

What if the randomness inside molecular docking actually matters?

What if the way we explore chemical space influences what we discover?

This is not a claim of revolution; it is a reflection on continuity.

Let’s go back to history. From the early 1990s to 2026, docking has always been about one thing: How do we explore an energy landscape efficiently?

Here is a brief arc of that journey:

 The Early 1990s:  The Birth of Automated Docking

In the early 1990s, AutoDock emerged under the leadership of Arthur J. Olson at The Scripps Research Institute. Computational resources were limited. Protein structures were fewer, yet the problem was clear: Predict how a small molecule binds to a protein.

In the early 1990s, AutoDock introduced empirical free-energy force fields, simulated annealing, and the Lamarckian genetic algorithm, a novel automated docking method that predicted the bound conformations of flexible ligands to macromolecular targets with unprecedented accuracy. The physics was classical. The exploration was stochastic. Simulated annealing borrowed from thermodynamics:

Heat → explore → cool → settle into low energy.

But the randomness driving this exploration came from pseudo-random number generators. And the annealing was metaphorical.

🚀 2010 — AutoDock Vina

In 2010, AutoDock Vina refined the architecture. It delivered:

• Major speed improvements

• Multithreading

• A redesigned scoring function

• Better convergence

The formula remained:

Energy model + Heuristic search + PRNG randomness

And it worked remarkably well. Vina became the backbone of countless academic and industrial pipelines.

2010–2020 — Scale and AI

Docking scaled.

• Millions → billions of compounds

• GPU acceleration

• Cloud-native screening

• AI-assisted scoring

Docking became part of larger discovery engines. But one assumption quietly remained untouched: The randomness driving search was deterministic.

 2020–2026 — Rethinking Randomness

This is where my personal journey intersects the timeline. While building QuantumCURE Pro™, I was not trying to reinvent docking physics. I was trying to understand something more fundamental:

Every docking simulation begins with the injection of randomness. Random seeds govern the initial ligand placement, rotational sampling, conformational jumps, and the critical escapes from local energy minima.

Which led me to ask: what if we change the entropy source?

Instead of PRNG:

• Use QRNG (physical quantum entropy), for example, I like the Cryptalabs devices.

• Use quantum annealing outputs, harvesting entropy from a real quantum annealer rather than a classical simulation of the annealing process.

• Track trajectory behavior symbolically, my Zaban™ framework assigns a unique glyph to each energy collapse zone, creating a linguistic map of molecular exploration paths that becomes a powerful input for AI-assisted drug candidate modeling.

Suddenly, the metaphor of simulated annealing meets physical annealing.

Modern hardware like D-Wave performs real annealing processes in superconducting circuits. QRNG devices derive entropy from physical quantum collapse events. Thirty years ago, docking simulated thermodynamic randomness. Today, we can inject physical entropy directly from a PCIe or USB device into the exploration process itself.

The Subtle Shift

Classic Docking:

Energy model + Heuristic search + Deterministic PRNG

Entropy-Aware Docking:

Energy model + Heuristic search + Physical entropy injection + Trajectory analysis. The force field remains classical. The scoring remains empirical. The shift is in exploration topology.

 Why This Matters

Optimization landscapes are rugged. Small perturbations can change pose diversity, convergence pathways, and scaffold distribution. Randomness is not neutral; it biases exploration. If different entropy sources produce statistically different sampling behavior, then entropy becomes part of the experimental design, not merely its setup.

That realization, discovered not in a well-funded lab but in a quiet Oklahoma office, felt like my small contribution. Not a new force field. Not a new docking engine. But a new question:

Does the source of randomness shape molecular discovery?

1990 → 2026 in One Arc

1990s: Simulated annealing powered by PRNG.

2010s: Massive scaling and limited AI integration.

2020s: Physical entropy enters optimization.

The story of docking is not a story of replacement. It is a story of refinement.

Perhaps the next chapter is not about rewriting the chemistry, but about understanding how the universe itself explores possibility space, and how that shapes what we find within chemical space. For a retired software engineer who followed curiosity into territory he had no business being in, that's more than enough reason to keep building.

https://dasher.wustl.edu/chem430/readings/jcc-19-1639-98.pdf#:~:text=A%20novel%20docking%20method%20that%20uses%20a%20%EE%80%80Lamarckian%EE%80%81

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