6 The role of AI and ML
AI has the potential to transform the future of the pharmaceutical industry. As datasets grow in number and diversity, the need for efficient and effective analysis is paramount, highlighting the significance of AI and ML technologies. AI can analyse masses of data, in a fraction of the time of conventional approaches without the risk of human error. These tools can be used to accelerate the prediction and optimisation of experiments, quickly identify anomalies, and empower decision-making.
Adequately engineered, quality data and an infrastructure for facile data management are key factors in the successful implementation of AI in pharmaceutical workflows. The maximal potential of AI can only be achieved if it is properly trained with good-quality datasets. Implementation and integration of appropriate chemistry software tools, like Method Selection Suite, can help create comprehensive and curated databases that can be used to supplement AI tools.
The software prepares data for machine use by homogenising and storing it with its chemical context - further enabling integration into AI initiatives by exporting the data in machine- readable format (i.e., JSON) ready for AI and ML applications.
Conclusion
Data analysis software tools are increasingly being leveraged to integrate and manage the vast amounts of data generated by pharmaceutical organisations, helping to eliminate siloed systems and empowering data accessibility. With the correct digital tools, collaboration is easier and scientists can preserve and leverage knowledge for future projects, data science, and deeper insights.
Digital transformation offers pharmaceutical organisations great potential. When done right, it should enhance processes to efficiently collect, analyse, and manage data, offer greater accessibility of data, and ensure that value can be extracted from the data to support informed data-driven decision-making.
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