Partner Search: AI and Predictive Modelling for Safe and Sustainable by Design Innovation

QSAR Lab can be involved in a consortium as a technology and knowledge provider in the field of artificial intelligence (AI), machine learning (ML), and predictive modeling for chemical safety and sustainability. QSAR Lab can support the development of innovative solutions by providing digital tools to identify, evaluate, and optimize advanced materials and processes across sectors. The company can offer guidance in designing AI-based workflows, ensuring regulatory compliance, and enhancing the Safe and Sustainable by Design (SSbD) approach. Thanks to its domain expertise, QSAR Lab brings deep knowledge in computational toxicology, nanoinformatics, and LCA modeling, aligning innovation with real-world regulatory and industrial needs.

Related to target TRLs:

• QSAR Lab can develop and deploy AI tools for identifying safe and sustainable alternatives to PFAS and other harmful substances, enabling rapid screening of candidate materials with desirable functional properties and minimal environmental or health risks.

• QSAR Lab can provide ML-based solutions for predicting the toxicity, biodegradability, and environmental persistence of Innovative Advanced Materials (IAMs), supporting early-stage material design under the SSbD framework.

• QSAR Lab can contribute to LCA-related work packages by integrating machine learning models into traditional LCA workflows, improving prediction of environmental impacts where empirical data is scarce or unavailable.

• QSAR Lab can lead the development of AI-driven data processing pipelines, automating data extraction, curation, and interpretation to support life-cycle thinking in chemicals and materials innovation.

• QSAR Lab can offer predictive modeling expertise to support recovery and recycling strategies of critical raw materials, identifying optimal process parameters and minimizing resource usage and waste generation.

• QSAR Lab can provide virtual screening and ML tools to support material design in the textile and construction sectors, enabling identification of alternatives to hazardous components and improving product durability and repairability.

• QSAR Lab can contribute to projects related to AI foundation models in materials science by offering expertise in training, fine-tuning, and applying domain-specific AI/ML algorithms for chemical and material property prediction.

QSAR Lab can guide the consortium on regulatory, toxicological, and environmental aspects of digital innovation, ensuring that newly developed technologies meet the expectations of industry stakeholders and policymakers while advancing towards sustainability goals.

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