Traditional material discovery is slow, costly, and inefficient: • Takes 5–15 years from concept to commercialization • Costs often exceed $100M • Less than 10% success rate due to trial-and-error approaches • R&D teams often lack AI capabilities to shorten the process Wellmatix cuts R&D time from years to months or weeks, reduces costs, and improves success rates by enabling in silico screening and data-driven decision-making.

Product Description: Wellmatix is an AI-powered SaaS platform for the rapid discovery and optimization of sustainable materials and drug candidates. It replaces slow, trial-and-error R&D with a fully digital pipeline that predicts properties, generates novel candidates, and ranks them based on performance, cost, and environmental impact — all before lab testing. Key Features: • Generative AI & Reinforcement Learning to design novel molecules/materials • Physics-informed modeling for accurate property prediction • Sustainability scoring (e.g., CO₂ footprint, recyclability) • LLM + RAG interface for literature-based reasoning and natural language queries • Virtual screening for ADMET, binding affinity, mechanical, thermal, and electronic properties • Custom dashboards for side-by-side comparison and AI-assisted decision-making Unfair Advantage: • Cross-domain AI engine trained on both materials and life sciences datasets • Proprietary multi-modal data fusion (SMILES, CIF, text, image, spectra) • Proven traction: Deployed in pilot projects with biotech and material companies in Korea, Japan & Europe • Integrated agentic AI architecture that automates the full R&D loop — from target definition to candidate recommendation Wellmatix stands out by enabling faster, greener, and smarter discovery while being accessible to both large enterprises and SMEs.