In Aitenea Biotech we believe that Artificial Intelligence (AI) must be understood beyond a simple technology capable of solving complex mathematic algorithms in science sectors

AI, together with science and innovation, has the potential to generate a wide average growth in the profitability and efficiency rates of multiple industries and to solve certain social problems more effectively and in less time


Combination of new molecules and AI in agriculture in order to achieve sustainable development, less toxic strategies, a reduction of diseases, etc


AI techniques to automatise tasks, optimise variables, developing and synthesising new molecules, transforming hydrocarbons, etc


Personalisation of cosmetics and tailor-made formulas, new compounds, etc


AI in the developing and repositioning of drugs, saving in research costs, a reduction in the failure index in clinical essays, development of better drugs, etc

Materials and compounds

Faster and more efficient process in the chemical industry, new materials and polymers, more ecological and sustainable compounds, etc


AI allows for more personalised and preventive healthcare, a better and more effective diagnosis of disease, etc

Promote growth and innovation with adapted solutions!

We offer personalised solutions to several sectors in need of chemical molecule prediction and counselling based on guaranteed methods and quality standards

We use Design Thinking techniques, Lean, Agile…

  • Identification, design and synthesis of new chemical compounds
  • Assistance in the study processes and clinical essays
  • Process automation
  • Development of predictive models adapted with AI techniques
  •  Improvement of business and operations

“Start getting predictions with the code SMILE”

Furthermore, with our platform Software as a Service (SaaS), the user can make predictions with an array of physicochemical properties with innovative AI techniques and large databases —all that with a single click. In order to do so, the user just needs to add the code SMILE in the structure they want to predict