Rapid planning and analysis of high-throughput experiment arrays for reaction discovery

Abstract

High-throughput experimentation (HTE) is an increasingly important tool in reaction discovery. While the hardware for running HTE in the chemical laboratory has evolved significantly in recent years, there remains a need for software solutions to navigate data-rich experiments. Here we have developed phactor™, a software that facilitates the performance and analysis of HTE in a chemical laboratory. phactor™ allows experimentalists to rapidly design arrays of chemical reactions or direct-to-biology experiments in 24, 96, 384, or 1,536 wellplates. Users can access online reagent data, such as a chemical inventory, to virtually populate wells with experiments and produce instructions to perform the reaction array manually, or with the assistance of a liquid handling robot. After completion of the reaction array, analytical results can be uploaded for facile evaluation, and to guide the next series of experiments. All chemical data, metadata, and results are stored in machine-readable formats that are readily translatable to various software. We also demonstrate the use of phactor™ in the discovery of several chemistries, including the identification of a low micromolar inhibitor of the SARS-CoV-2 main protease. Furthermore, phactor™ has been made available for free academic use in 24- and 96-well formats via an online interface.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

“…There is already work on developing automated evolution systems and integrating these into active learning workflows where data generated from automated experiments can train and refine ML models to suggest beneficial variants to explore further. ,, These “design-build-test-learn” cycles would enable continuous optimization of enzymes and other proteins (Figure ), as they can for small molecules . LLMs could power these automated systems, with AI flexibly adapting to perform new types of syntheses and screens with robotic scripts written on the fly. − At the same time, multiple desirable properties and activity for multiple reactions could be optimized simultaneously during protein engineering campaigns, powered by generalized ML models that can utilize multimodal representations of proteins. With ever increasing amounts of data on protein structures and sequence-fitness pairs, and new tools to conduct experiments − and make ML methods for proteins more accessible to the broader community, the future of ML-assisted protein engineering is bright.…”

Reaction Space Charting as a Tool in Organic Chemistry Research and Development

“…To better address this point, Cernak lab has developed Phactor™, a high throughput screening platform to handle experiment planning and output analysis of reaction discovery campaigns, broadcasted as a free web service for the scientific community. [87] Cronin has showed how an automated robotic set-up can handle a reaction discovery process from design to analysis. [88]…”

Organopalladium Catalysis as a Proving Ground for Data-Rich Approaches to Reaction Development and Quantitative Predictions

“…Researchers at Merck used a combination of advanced liquid handling automation with rapid UPLC-MS or MALDI-TOF MS analysis to interrogate thousands of Pd-catalyzed C–N coupling reactions in plate-based formats. , Researchers at Pfizer similarly demonstrated a new flow-based HTE platform using Pd-catalyzed Suzuki–Miyaura couplings to run and analyze thousands of examples . In academia, the Cernak group is continuing to push the envelope of ultrahigh throughput methods in array design and execution. − In the area of laboratory automation, the Hein group, in collaboration with Merck and the Aspuru-Guzik and Sigman groups, demonstrated the power of data-dense experimentation in an autonomous optimization of a Pd-catalyzed Suzuki–Miyaura reaction . The Newman group published a user-centered “how-to-HTE” guide on multiwell screening, using a Pd-catalyzed C–N coupling as the prototype reaction .…”