Making HTE Easier and More Accessible to Scientists

The benefits of high throughput experimentation, particularly in the chemistry laboratory, cannot be overstated. In R&D organizations, where trial & error is a part of a scientist’s everyday life, nothing can be better than having the ability to test multiple knowledge-driven hypotheses simultaneously. Yet, there are still many lab-based chemists that will serially test their synthetic hypotheses (new transformations, reactivity, solvent effects, etc.), often overstressing how the results of each experiment will provide important learnings for future iterations. While this is arguably true, a well-designed array of carefully controlled experiments, will almost always supply you with improved conditions for a specific transformation, and usually faster than any serial journey of single reactions. So, if that is true, more often than not, then why do researchers resist efficiency and reproducibility and default to what is familiar—serial experiments? One simple answer is basic human behavior—our aversion to change.

For change to occur in any organization, there must be a demonstrable benefit to the individual scientist, as well as the collective organization, but maybe more important than that, the cost of change, cannot exceed the benefit to those being asked to change their work routines. In this case the cost to those making the changes is perceived and real, as measured by personal frustration and by temporary productivity loss during the sometimes-unavoidable transition period. Lowering the cost of this change by simplifying the integration and adoption of semi-automated and automated kit, and improved ELNs to easily orchestrate and capture experimental results into daily workflows, is a paramount objective for software and hardware suppliers.

In this webinar ACD/Labs, ChemSpeed, Integrated Lab Solutions, and Sapio Sciences will describe their efforts to simplify acceptance so that, when appropriate, high throughput experimentation becomes the default choice for scientists that need to optimize a specific chemical transformation.