Nawah Scientific, a multidisciplinary scientific research, analytical, and drug development platform, and Pauling.AI, a US-based company specializing in AI-powered computational drug discovery, have announced a strategic partnership to provide pharmaceutical and biotechnology companies worldwide with an integrated drug discovery offering combining computational research with experimental laboratory validation.
Challenges
The partnership aims to address one of the long-standing challenges in drug discovery: the gap between computational predictions and experimental evidence.
Under the partnership, Pauling.AI will use its autonomous computational drug discovery platform to transform biological targets and research hypotheses into prioritized molecular candidates through workflows covering virtual screening, molecular docking, molecular dynamics, binding-affinity analysis, ADMET prediction, and candidate prioritization.
Experimental Validation
Nawah Scientific will then bring these computational proposals into the laboratory through tailored experimental validation, including biochemical and cell-based assays, analytical characterization, early efficacy studies, pharmacokinetic investigations, and other preclinical services.
The two companies will provide clients with a unified scientific and commercial engagement connecting computational discovery, laboratory experimentation, data generation, and data-driven iteration within a coordinated workflow.
Closing the Gap Between Prediction and Proof
AI can explore chemical space at a scale that traditional drug discovery teams may struggle to match. However, computational predictions remain hypotheses until they are tested against biological reality.
Through the new partnership, Pauling.AI will identify and prioritize high-potential molecules before experimental work begins, while Nawah Scientific will generate the experimental evidence required to confirm biological activity, evaluate performance, and identify the most promising candidates for further development.
Additional Computational Cycles
The resulting experimental data can then feed back into additional computational cycles, creating a continuous discovery loop in which predictions are tested, results are analyzed, and subsequent searches are refined based on real-world evidence.
The integrated model is designed to help pharmaceutical and biotechnology companies screen larger chemical spaces without expanding their internal infrastructure, focus laboratory resources on higher-potential molecules, reduce spending on low-probability candidates, and accelerate the cycle between prediction, testing, and molecular optimization.
Flexible Computational
It also enables companies to access flexible computational and laboratory capabilities without having to build additional specialized teams or infrastructure.
“This partnership represents the drug discovery model of the future: intelligence and experimentation operating as one continuous system,” said Dr. Omar Sakr, Founder and CEO of Nawah Scientific.
“Pauling.AI can explore molecular possibilities at extraordinary speed. Nawah provides the experimental power to challenge those possibilities, generate real biological evidence, and determine what deserves to move forward. We are not simply connecting two service providers, we are building a faster route from scientific imagination to experimentally supported opportunity,” he added.
The Value Emerges
Javier Tordable, CEO of Pauling.AI, said that computational discovery should not end with a ranked list of molecules.
“The value emerges when predictions are tested, the resulting evidence is understood, and the system learns where to search next,” Tordable said. “By combining Pauling.AI’s autonomous computational platform with Nawah’s extensive wet-lab capabilities, clients can move from target to testable molecules through a single, tightly coordinated workflow.”
Targeting the Next Generation of Pharma and Biotech Companies
The joint offering is designed for pharmaceutical companies, biotechnology ventures, virtual biotech teams, academic spinouts, and investor-backed drug discovery programs across Europe, the United States, and other global markets.
The partnership is particularly aimed at organizations seeking to expand their drug discovery capabilities, explore new or underexplored biological targets, validate AI-generated candidates, accelerate hit identification, or launch discovery programs without making significant upfront investments in specialized infrastructure and multidisciplinary teams.
Integrated Team
Instead of transferring projects between separate computational vendors and contract laboratories, clients will have access to an integrated team capable of coordinating molecular selection, experimental design, data generation, and subsequent computational and experimental iterations.
The companies said the approach creates a more connected drug discovery engine in which AI proposes potential molecules, laboratory biology tests them, data informs the next search, and the cycle continues until the strongest candidates emerge.