Nawah Scientific Partners Pauling.AI on Drug Discovery

In Short

Nawah Scientific and Pauling.AI have formed a strategic partnership to offer an integrated drug discovery service. The goal is to connect AI-based molecular design with wet-lab validation in one workflow.

What Happened

Under the deal, Nawah Scientific will work with Pauling.AI, a US company focused on AI-powered computational drug discovery. Together they want to serve pharma and biotech clients globally with a combined “predict and test” model.

Pauling.AI will run computational workflows that turn a biological target into a ranked list of molecules. These steps include virtual screening, which is a fast computer search through many possible compounds. They also include molecular docking, which is a simulation of how well a molecule might fit into a protein, like a key in a lock.

The workflow also covers ADMET prediction, which estimates absorption, distribution, metabolism, excretion, and toxicity, basically early signals of whether a compound could work as a medicine and be safe enough to continue. After that, candidates are prioritised before any lab work starts.

Nawah Scientific will then validate the shortlisted molecules in the lab using biochemical assays and cell-based assays, which are controlled tests that measure activity in enzymes or living cells. It will also handle analytical characterisation, early efficacy checks, and pharmacokinetic investigations, which study how a compound moves through the body.

Both companies say the experimental results will feed back into new computational cycles. That creates an iteration loop where predictions are tested, data is generated, and the next search is refined using real evidence.

Why It Matters

Drug discovery often breaks down between promising computer predictions and what actually works in biology. This partnership is designed to close that gap by coordinating the handoff from algorithms to experiments.

For pharma and biotech teams, the pitch is practical. They can screen larger chemical spaces without building extra in-house infrastructure. Lab time can be focused on higher-probability molecules, which could reduce spend on dead ends and shorten timelines from target selection to hit identification.

The joint offering also targets virtual biotech teams and academic spinouts that may not have full-stack discovery capabilities. Instead of managing separate computational vendors and contract labs, they get one coordinated setup for molecular selection, experimental design, and repeat testing.