Transforming Science with AI & Machine Learning
Data - Driven Solutions
Through the combination of industry-recognized expertise, state-of-the-art software, and proprietary computing infrastructure, NovaMechanics’ advanced in silico capabilities in molecular design and simulation—powered by cutting-edge Machine Learning (ML) and Safe and Sustainable by Design (SSbD) principles—provide the most effective path to innovation in drug discovery and materials science.

Managing Director


Harnessing AI & Computational Science for Breakthrough Innovation
Advancing Science Through AI & Computational Innovation
As a research-driven company, NovaMechanics actively participates in large-scale European and national R&D projects, applying AI-powered cheminformatics, nanoinformatics, and bioinformatics to solve challenges in personalized medicine, nanotoxicology, and materials informatics. With a 30-member multidisciplinary team, including 17 PhD holders, NovaMechanics combines deep scientific expertise with state-of-the-art computational infrastructure to develop validated, industry-ready solutions.
Empowering Innovation Through AI, Machine Learning, and Computational Science
NovaMechanics (NovaM) is a pioneering SME with headquarters in Nicosia, Cyprus, and operations in Athens, Greece, specializing in data-driven scientific solutions at the intersection of cheminformatics, bioinformatics, nanoinformatics, artificial intelligence (AI), machine learning (ML), and computational modeling. With over 15 years of expertise, NovaM develops cutting-edge software tools, predictive models, and cloud-based platforms, transforming drug discovery, materials science, and risk assessment through computational innovation.
At NovaMechanics, we integrate AI, ML, and advanced computational techniques to extract meaningful insights from complex datasets, enabling high-precision modeling, data-driven decision-making, and in silico experimentation. Our work spans multiple scientific domains, from predictive toxicology and molecular design to safe and sustainable material development, leveraging big data analytics, automation, and multiscale simulations to streamline research and innovation.


