Drug Discovery Before Computers — And Why We’re Entering a New Era

mansour ansari
Jan 21
4 min read

I come from the old era of computing. I started with a clone PC back in the 80s, then upgraded to an IBM PC/AT with 640k RAM and a whopping 10 MB HD. It is still sitting in my closet, and I love to look at it sometimes. Now, 40 years later, I am designing a Cancer Drug Discovery in my back office using a modern PC. Just out of curiosity, I did some research on old drug-discovery systems/methods before PCs and wrote this post.

So, from the 1960s to the 1980s, long before tools like AutoDock Vina (the one that I have deployed to CLOUD for the first time, before anyone else done it!) brought virtual docking to the cloud (done by me!), drug discovery was an entirely physical endeavor, built on intuition, trial-and-error, and manual effort by skilled chemists and pharmacologists. Today, as we move into a new computational epoch fueled by AI, quantum entropy, and symbolic modeling, it’s worth remembering how far we’ve come, and how radically different the next five years might be.

The Pre-Digital Drug Discovery Era

1. Phenotypic Screening: Biology First

Before target-based drug design, the industry relied on phenotypic screening — testing thousands of compounds on living systems (cells, tissues, animals) to see if anything worked. This “try it and see” method required no understanding of molecular mechanisms. It was slow, expensive, and highly empirical — but it sometimes worked.

Many blockbuster drugs were found this way, not because someone understood the binding mechanism, but because someone saw an unexpected effect in a lab rat.

2. Nature Was the Laboratory

The pharmaceutical world mined nature for leads. Plants, fungi, and microbes were extracted, fractionated, and tested for biological activity. Natural products like penicillin, vincristine, and aspirin derivatives emerged through this labor-intensive process.

Nature was the original drug library — and many of its secrets are still only partially explored.

3. Medicinal Chemistry: By Hand, By Intuition

Once an active compound was identified, medicinal chemists began crafting analogs by hand, studying structure-activity relationships (SAR) without software. Each modification required synthesis, purification, and testing. Decades of human experience guided these changes.

There were no scores, no virtual predictions, just test tubes, lab notebooks, and guts.

4. Physical Models and Paper Calculations

With no molecular modeling software, chemists built ball-and-stick models of molecules and estimated binding potential using hand-drawn structures, physical overlays, and crude calculators. Early QSAR equations were solved by hand or on slide rules.

5. Animal Testing and Serendipity

The most important validation came from animal trials. Many compounds failed late, after years of work. Others succeeded by accident, like Minoxidil (originally a blood pressure drug, later Rogaine) or Thalidomide (now used for cancer after its tragic origin).

Discovery was often a byproduct of serendipity — not strategic design.

The Limitations Were Severe:

Slow — timelines of 10–20 years.
Costly — huge investment per compound.
Low-throughput — small chemical libraries, manually handled.
Mechanistically opaque — often no understanding of why something worked.

Then Came Computers — But That Was Just the First Leap

By the 1990s:

X-ray crystallography revealed protein structures.
Docking software like AutoDock and later AutoDock Vina became viable.
Early virtual screening reduced lab work dramatically.

Computational chemistry began to augment — and sometimes replace — the wet lab. But even today, many pipelines still rely heavily on pseudo-random number generators (PRNG) for simulations and static libraries for ligand testing.

And that’s where a new leap begins.

My Role — A Small Step with Big Implications

I’m not a chemist. I don’t work in pharma. I’m not backed by a major lab or billion-dollar fund. But I am skilled in processing large data, building systems, and arranging binaries at scale, a skill that I acquired over the years and that is now intensified by modern AI systems.

Over the last two years, I’ve built and deployed a cloud-based system that uses:

AutoDock Vina at cloud scale, running simulations across compound libraries
Quantum entropy inputs (via QRNG and D-Wave annealing) instead of classical randomness
Symbolic glyph encoding of collapse patterns
Web-based orchestration and control of full simulation pipelines

This is not a toy project. It is real, tested, and deployed. I call it QuantumCURE Pro™, and it stands as a potential new way to discover drug leads — not by imitating legacy systems, but by innovating past them.

🧭 Looking 5 Years Ahead: A Post-NISQ Future

We’re still in the NISQ (Noisy Intermediate-Scale Quantum) era. But it's fading. As fidelity increases and quantum-classical hybrids become standard:

AI will no longer augment discovery — it will initiate and filter ideas.
Quantum systems will no longer be used to generate novelty — they’ll be tuned to collapse symbolic states for precise simulation.
Language models, symbolic encoders, and quantum entropy generators will co-pilot the design of molecular scaffolds, lead compounds, and biologics.