AI and machine learning in pharmaceutical formulation and manufacturing of personalized medicines

Personalized medicine refers to the transformative approaches adopted in diagnosis and disease evaluation that use a patient's genetic and molecular profiles to guide and design effective treatments. The driving forces behind the emergence of this field are advancements in omics technologies and availability of staggering amounts of data that can be mined to extract meaningful information. Moreover, an increasing understanding of the biological underpinnings of diseases like cancer has added to this large data space, aiding the development of a new generation of therapeutics, ultimately delivering better outcomes for patients. While the prospect of personalized medicine looks favorable with availability of big data, integrating and analyzing such complex data can be challenging. Artificial intelligence can be leveraged in the field of pharmaceutical development to assist human-led efforts and deliver safe and effective drug formulations. Machine learning and artificial neural networks can be used to advance evidence-based medicine improving cost effectiveness and reducing clinical trial failures. To understand the contributions of artificial intelligence in creating potent drugs, the authors of this chapter attempt to present a bird's eye view of the field.