Machine learning‐driven protein engineering: a case study in computational drug discovery. Issue 1 (16th March 2020)

• Record Type: Journal Article
• Authors: Rickerby, Harry F.; Putintseva, Katya; Cozens, Christopher
• Is Part Of: Engineering biology. Volume 4:Issue 1(2020)
• Page Start: 7
• Page End: 9
• Publication Date: 2020-03-16
• Subjects: proteins -- biology computing -- learning (artificial intelligence) -- drugs -- molecular biophysics -- optimisation -- DNA; machine learning‐driven protein engineering -- computational drug discovery -- significant efficiency gains -- drugs -- great expectation -- high‐quality datasets -- high‐throughput display -- selection data generation -- deep learning -- directed evolution -- DNA library synthesis -- ultra‐high throughput selections -- generation sequencing -- learnings -- silico models -- multiple important protein characteristics -- ML‐driven drug discovery; Bioengineering -- Periodicals; Bioengineering; Periodicals; 660.605
• DOI: 10.1049/enb.2019.0019
• Languages: English
• ISSNs: 2398-6182

Abstract

Abstract : Research and development in drug discovery will need to find significant efficiency gains if the industry is to continue generating novel drugs. There is great expectation for machine learning (ML) to provide this boost in R&D productivity, but to harness the full potential of ML, the generation of new, high‐quality datasets will be necessary. Here, the authors present a platform that combines high‐throughput display and selection data generation with ML. More specifically, deep learning is used to inform the directed evolution of novel biotherapeutics using DNA library synthesis, ultra‐high throughput selections, and next generation sequencing. By combining the learnings of multiple in silico models, their platform enables multi‐parameter optimisation across multiple important protein characteristics. They also present a model for benchmarking these ML‐driven drug discovery platforms according to the accuracy of their underlying in silico models, in conjunction with the throughput of their empirical experimentation.