High Throughput Screening for Drug Discovery and Virus Detection

Background: High throughput screening systems are automated labs for the analysis of many biochemical substances in the drug discovery and virus detection process. This paper was motivated by the problem of automating testing for viruses and new drugs using high throughput screening systems. The emergence of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) at the turn of 2019-2020 presented extraordinary challenges to public health. Existing approaches to test viruses and new drugs do not use optimal schedules and are not efficient. Objective: The scheduling of activities performed by various resources in a high throughput screening system affects its efficiency, throughput, operations cost, and quality of screening. This study aims to minimize the total screening (flow) time and ensure the consistency and quality of screening. Methods: This paper develops innovative mixed-integer models that efficiently compute optimal schedules for screening many microplates to identify new drugs and determine whether samples contain viruses. The methods integrate job-shop and cyclic scheduling. Experiments are conducted for a drug discovery process of screening an enzymatic assay and a general process of detecting SARS-CoV-2. Results: The method developed in this article can reduce screening time by as much as 91.67%. Conclusion: The optimal schedules for high throughput screening systems greatly reduce the total flow time and can be computed efficiently to help discover new drugs and detect viruses.