An Automated Robotic Interface for Assays: Facilitating Machine Learning in Drug Discovery by the Automation of Physicochemical Property Assays.

Measuring the physicochemical properties of molecules is an iterative but integral process in the drug development process. A strategy to overcome the challenges in maximizing assay throughput relies on the usage of in silico machine learning (ML) prediction models trained on experimental data. Consequently, the performance of these in silico models are dependent on the quality of the utilized experimental data. To improve the data quality, we have designed and implemented an automated robotic system to prepare and run physicochemical property assays (Automated Robotic Interface for Assays, ARIA) with an increase in sample throughput of 6 to10-fold. Through this process, we overcame major challenges and achieved consistent reproducible assay data compared to semiautomated assay preparation.

ReachingBot: An automated and scalable benchtop device for highly parallel Single Pellet Reach-and-Grasp training and assessment in mice.

BACKGROUND: The single pellet reaching and grasp (SPRG) task is a behavioural assay widely used to study motor learning, control and recovery after nervous system injury in animals. The manual training and assessment of the SPRG is labour intensive and time consuming and has led to the development of multiple devices which automate the SPRG task. NEW METHOD: Here, using robotics, computer vision, and machine learning analysis of videos, we describe a device that can be left unattended, presents pellets to mice, and, using two supervised learning algorithms, classifies the outcome of each trial with an accuracy of greater than 94% without the use of graphical processing units (GPUs). Our devices can also be operated using our cross-platform Graphical User Interface (GUI). RESULTS: We show that these devices train and assess mice in parallel. 21 out of 30 mice retrieved > 40% of pellets successfully following the training period. Following ischaemic stroke; some mice showed large persistent deficits whilst others showed only transient deficits. This highlights the heterogeneity in reaching outcomes following stroke. COMPARISON WITH EXISTING METHOD(S): Current state-of-the-art desktop methods either still require supervision, manual classification of trial outcome, or expensive locally-installed hardware such as graphical processing units (GPUs). CONCLUSIONS: ReachingBots successfully automated SPRG training and assessment and revealed the heterogeneity in reaching outcomes following stroke. We conjecture that reach-and-grasp is represented in motor cortex bilaterally but with greater asymmetry in some mice than in others.

Compound Transfer by Acoustic Droplet Ejection Promotes Quality and Efficiency in Ultra-High-Throughput Screening Campaigns.

Acoustic droplet ejection (ADE) as a means of transferring library compounds has had a dramatic impact on the way in which high-throughput screening campaigns are conducted in many laboratories. Two Labcyte Echo ADE liquid handlers form the core of the compound transfer operation in our 1536-well based ultra-high-throughput screening (uHTS) system. Use of these instruments has promoted flexibility in compound formatting in addition to minimizing waste and eliminating compound carryover. We describe the use of ADE for the generation of assay-ready plates for primary screening as well as for follow-up dose-response evaluations. Custom software has enabled us to harness the information generated by the ADE instrumentation. Compound transfer via ADE also contributes to the screening process outside of the uHTS system. A second fully automated ADE-based system has been used to augment the capacity of the uHTS system as well as to permit efficient use of previously picked compound aliquots for secondary assay evaluations. Essential to the utility of ADE in the high-throughput screening process is the high quality of the resulting data. Examples of data generated at various stages of high-throughput screening campaigns are provided. Advantages and disadvantages of the use of ADE in high-throughput screening are discussed.