Perspectives on Supercomputing and Artificial Intelligence Applications in Drug Discovery

This review starts with outlining how science and technology evaluated from last century into high throughput science and technology in modern era due to the Nobel-Prize-level inventions of combinatorial chemistry, polymerase chain reaction, and high-throughput screening. The evolution results in big data accumulated in life sciences and the fields of drug discovery. The big data demands for supercomputing in biology and medicine, although the computing complexity is still a grand challenge for sophisticated biosystems in drug design in this supercomputing era. In order to resolve the real-world issues, artificial intelligence algorithms (specifically machine learning approaches) were introduced, and have demonstrated the power in discovering structure-activity relations hidden in big biochemical data. Particularly, this review summarizes on how people modernize the conventional machine learning algorithms by combing non-numeric pattern recognition and deep learning algorithms, and successfully resolved drug design and high throughput screening issues. The review ends with the perspectives on computational opportunities and challenges in drug discovery by introducing new drug design principles and modeling the process of packing DNA with histones in micrometer scale space, a n example of how a macrocosm object gets into microcosm world.