LabAutomation
Pipetting robot in a
lab.Close- up of the pipettes in a robotic pipetting machine in a molecular epidemiology laboratory. Photograph courtesy of Arno Massee/Science Photo Library
deep learning model pipeline, making science faster, more efficient and more accurate.
Chemistry automation – computation, synthesis and prediction Prognosis: available and maturing As a discipline, chemistry has existed for thousands of years and its practice on industrial scales in recent history dates back at least a few centuries. As such, AI/DL and machine learning (ML) can more readily be applied here: multiple large data sets exist in the public domain, albeit of varying quality and reproducibility, and all large compa- nies have proprietary molecular databases contain- ing molecular descriptors, predicted properties, safety thresholds and assay data against multiple targets of interest. Most applications in artificial intelligence and
the subset field of machine learning in chemistry are concerned with predicting three things: which molecules tomake, how tomake themand predict- ing properties or safety panel data for the molecules thus synthesised. To be clear, applying algorithmic intelligence to synthetic pathways is not new: Nobelist E.J. Corey explored symbolic logic and computational route-finding with his LHASA and OCSS already in the late 1960s5. Chemistry AI generally, andmachine learning from text-mining specifically, has been used to great effect. One of us (MAT) assisted a Novartis team
Drug DiscoveryWorld Summer 2019
with a text-mining and predictive reaction assign- ment of 30 years of patent literature, seeking to describe en masse the types of molecules and phys- ical properties produced by pharmaceutical and biotech firms6. Retrosynthetic analysis now com- monly runs on multiple commercial platforms, such as SciFindern (formerly ChemPlanner)7 or MilliporeSigma’s Synthia (formerly Chematica). Both systems utilise machine learning to predict routes based on combinations of expert rule-sets and literature-derived reaction capture8. Multiple companies have begun to use fully AI-
based approaches to target selection, drug design, property prediction or synthetic execution. Cyclofluidic utilised AI-based algorithms to anal- yse product output to inform future synthesis rounds, an approach adapted and refined by Exscientia. Nimbus Therapeutics claims to utilise bespoke computational tools to shorten drug dis- covery development times by 75%. Revolution Medicines, which spun out from synthetic automa- tion efforts at the University of Illinois – Urbana- Champaign – uses a proprietary computational pipeline against oncology targets, while Recursion Pharmaceuticals mines phenotypic data to con- struct models for ideal drugs9. Though not itself AI, data collection and automation efforts on an academic scale; fully digital synthetic ‘engines’ have sprung up in labs such as Lee Cronin’s at The University of Glasgow10.
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