Machine Learning with Digital Microfluidics for Drug Discovery and Development

Drug discovery and development plays an increasing important role in biopharmaceutical field. However, current drug discovery is slow, expensive, inefficient and error prone. Digital microfluidic platform is potentially a very precious technique for drug discovery applications. In addition, machine learning algorithms have been used as a useful tool in drug discovery. New technological shifts in the fields of drug discovery can rely on computer-aided design algorithms in digital microfluidics to acquire data and machine learning algorithms to analyze date, in order to improve throughput and reliability, especially in the context of complex bioassays with different formulations. This paper reviews current and future applications of microfluidic in drug discovery using machine learning and highlight the potential of a new design which combine digital microfluidics with machine learning algorithms for drug discovery applications.