Machine Learning for Biomanufacturing Processes

Sample Company White Paper

The pharmaceutical industry is increasingly competitive. Small pharmaceutical companies need increased efficiency and reduction of errors that algorithms can provide versus traditional manual labor in order to be competitive with larger companies. Machine learning can be used to help you understand your molecule by processing virtual chemical libraries. This approach can be used in each of the 4 stages of a clinical trial and accelerate the manufacturing phase. Our software can improve quality and efficiency. By reducing errors and defects using large chemical databases and algorithms trained to recognize patterns in this data, companies can reduce variation from expected experimental outcomes as measured in lab. This potentially saves time and production costs on labor and R&D by reducing waste prior to performing experiments in lab with high levels of accuracy. Moreover, working analytics and metrics into your data management pipeline using IT can help you streamline decisions across your organization. Analytics reports can help you visualize data and communicate ideas so you can focus on the best experiments to perform. These reports can be documented in databases accessible to anyone in your organization who has access to your private cloud, assisting in clinical trials and the regulatory process. By leveraging the cloud, we hope to make your organization more connected. Ideally, this could help you reduce bad batches and clinical runs or wet lab tests and standardize your workflows. More…