Quantumzyme and Predictive Research Sign MoU to Explore AI in Green Chemistry and Biocatalysis
Quantumzyme Corp. (OTC: QTZM) has signed a Memorandum of Understanding with Predictive Research Inc. to evaluate AI-enabled applications across enzyme engineering, biocatalysis, and sustainable chemical development. The agreement is exploratory, but the scope is practical: apply machine learning and data science to speed up R&D, improve enzyme design, and guide greener process choices.
For R&D teams, the signal is clear. Computational enzyme engineering is moving closer to AI-native workflows, with an emphasis on measurable gains in activity, selectivity, stability, and process efficiency.
What each party brings
Quantumzyme focuses on biotransformation and computational enzyme engineering. Its QZyme Workbench platform uses quantum mechanics-informed modeling, molecular simulations, and computational design to optimize enzyme performance across pharma, specialty chemicals, fragrances, food ingredients, and textiles.
Predictive Research adds depth in generative AI, NLP, machine learning, and advanced analytics. The company works across healthcare, life sciences, finance, retail, telecommunications, and automotive-useful experience for scaling data pipelines and model deployment.
Collaboration focus areas under the MoU
- AI-supported approaches to enzyme engineering and green chemistry research
- Data-driven methodologies to inform sustainable biotransformation concepts
- AI-enabled insights from complex biological and chemical datasets
- Structured knowledge sharing between scientific and data science teams
Why this matters for technical teams
Linking QM/MM-style insights with ML can cut the cycle time from hypothesis to validation. Expect attention on feature engineering from sequence, structure, and reaction context; active-site design and mutational scanning; and model-informed process conditions that reduce solvents, steps, or byproducts.
On the data side, success hinges on curation and lineage: assay normalization, negative result capture, metadata standards, and reproducible pipelines. Teams will likely need knowledge graphs and LLM-assisted literature synthesis to stitch together sparse, heterogeneous datasets.
Executive commentary
Naveen Krishnarao Kulkarni, Chief Executive Officer of Quantumzyme Corp., said, "This MoU reflects our interest in evaluating how advanced AI and data-driven approaches may complement our computational enzyme engineering platform. While still at an exploratory stage, collaborations such as this may help inform future research directions as we continue to pursue more sustainable solutions in green chemistry."
Dr. Basavaraj S. Patil, Chief Data Scientist at Predictive Research Inc., added, "We view this MoU as an opportunity to explore how generative AI and predictive analytics could be applied within complex biochemical and catalytic research environments. By combining complementary technical perspectives, both organizations aim to assess new approaches to data-informed innovation in life sciences."
Context and practical next steps
If you're running enzyme programs, this is a prompt to audit your data foundations and model-readiness. Start with standardized assay schemas, versioned datasets, and clear objective functions (e.g., Ξkcat, ΞKm, E-factor).
- Prioritize sequence-to-function models that incorporate structure (AlphaFold-derived features), electrostatics, and substrate context.
- Pilot active learning loops: propose variants, batch test, retrain, repeat.
- Quantify environmental impact early with metrics aligned to green chemistry principles.
Where to learn more
For foundational perspective on green chemistry principles, visit the ACS Green Chemistry Institute. For updates from the companies, see Quantumzyme's website and its OTC Markets profile.
If your lab is building AI skills for R&D workflows, explore practical learning paths by job role: Complete AI Training - Courses by Job.
About Quantumzyme Corp.
Quantumzyme is a biotransformation company focused on sustainable enzyme-based solutions for pharmaceutical manufacturing. The company integrates quantum mechanics, molecular modeling, AI-driven simulations, and computational enzyme engineering to develop biocatalysts aimed at higher efficiency, lower waste, and scalable, environmentally responsible production.
Note on forward-looking statements
The MoU sets a framework for exploration and does not guarantee specific outcomes. Plans, expectations, and projections may change based on data, validation results, and market or technical constraints.
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