Our software tool provides predicted properties of (bio) chemicals based on data-driven algorithms and quantum chemistry, which helps to reduce the number of experiments in R&D. Every (bio)chemical process or product needs physico-chemical properties (e.g. solubility, density) data to simulate unit operations and flowsheets of chemical manufacturing processes. Also, the formulation of products such as more sustainable solvents or more efficient materials need property calculations for designing these products. Scientists and engineers at biotech companies usually fail to develop and simulate new processes and products because: (I) the experimental data for specific molecules are not available, (II) no physical property models have been developed for the desired compounds which give satisfiable predictions, (III) measurements are too time- and material-consuming to perform. MQS has developed a software which provides novel property prediction models based on machine learning and quantum chemistry (Figure 1), in which the input from experiments can be reduced or is negligible. Therefore the number of experiments, material, labour and time are saved which translates to reducing R&D costs in a 407 billion $ industry [1,2,3]. [1] EvaluatePharma – World Preview 2019, Outlook to 2024, June 2019; [2] Edelman, B., Biotechnology Healthcare 2004 May; 1(2): 37–41; [3] Global Market Insights, Inc, Sellbyville, Delaware, Jan. 23, 2019 (GLOBE NEWSWIRE)