GeneTropica
Cover 01 Problem 02 Idea 03 Method 04 Rigor 05 Results
A computational drug-repurposing study · Russell Young · 2026

Finding new medicines hidden in old ones.

A search for forgotten cures: screening 100 approved drugs against the proteins behind dengue and other neglected tropical diseases, using physics, chemistry, and a measured dose of machine learning.

Fig. 1, a candidate ligand, abstracted
01 / The problem

A billion people, and almost no new drugs.

Neglected tropical diseases like dengue, chikungunya, and leptospirosis affect more than a billion people, and Indonesia carries a disproportionate share of that burden.

Yet building a brand-new drug takes well over a decade and a billion dollars. For diseases that mostly affect the world's poorest, that investment rarely comes. The pipeline is, in practice, empty.

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people at risk worldwide
02 / The idea

What if the cure already exists?

Drug repurposing asks a simple question: instead of inventing a new molecule, could a medicine we already trust, already proven safe in millions of people, work against a disease it was never designed for?

It is faster, cheaper, and safer at the starting line, because the hardest question, "is this safe for humans," already has an answer. GeneTropica screens a curated library of approved drugs to find those hidden second lives.

03 / The method

Seven stages, from molecule to candidate.

i

A curated library

100 FDA-approved drugs, chosen to span chemical space, become the search pool.

ii

Six disease targets

Key proteins from dengue, chikungunya, and leptospirosis, the machinery each disease depends on to survive.

iii

~1,800 docking simulations

AutoDock Vina computes how snugly every drug fits into every target, like testing keys against a lock.

iv

Two honest rankings

Candidates are scored both by raw binding strength and by ligand efficiency, the binding earned per atom, shown side by side.

v

A machine-learning prior

A model trained on known actives adds a hint of likely activity. It is treated as a clue, never the verdict.

vi

Safety screening

ADMET filters keep only drug-like, tolerable molecules, the ones a body could plausibly absorb and clear.

vii

Molecular dynamics

The strongest candidates are stress-tested with 50-nanosecond simulations to see whether the fit actually holds.

04 / Rigor

Built to be doubted.

Good science earns trust by showing its limits, not hiding them. GeneTropica is built around that idea.

A known antiviral, sofosbuvir, is included as a positive control: if the method is sound, it should recognise a drug we already know works. Where the model is weak, that weakness is published openly, including a validation score of 0.37 on one dengue target. The machine learning is a prior, not a proof.

A result you can't question isn't science.

05 / Results

What the search turned up.

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approved drugs
screened
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protein targets
across 3 diseases
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docking
simulations
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drug-like
candidates
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ADMET-safe
candidates
0 ns
dynamics per
finalist

Against dengue's replication machinery, two families of drug rose to the top: nucleoside analogues and hepatitis-C antivirals, classes that already disrupt viral copying in other diseases. Promising leads, openly caveated, ready for a wet lab to test.

Nucleoside analogues
Hepatitis-C antivirals
Dengue NS3 & NS5
The interactive study

Explore every drug, target, and 3D binding pose yourself.