As automation in scientific experimentation advances, there is an increasing need for intelligent experimental design tools that can find optimal targets in large, complex experimental spaces. Traditional experimental design methods pare down large spaces by reducing their dimension (that is, the number of their components, such as system constituents, experimental parameters, etc.) until they are small enough to be explored exhaustively. But as the spaces being searched become more and more complex, these methods become increasingly uneffective, because they fail to identify those beneficial, synergistic inter-component interactions that can only be observed when the space is explored in its original dimension. These interactions are unpredictable, because they typically cannot be inferred from basic chemical and physical laws, but finding them is essential, because they are often the building block that makes a target truly optimal.
ProtoLife's Predictive Design Technology™ (PDT) efficiently solves this problem. PDT is an automated, intelligent predictive modeling tool that finds optimal or quasi-optimal targets in huge, complex experimental spaces without exhaustive exploration.
PDT iterative approach enables your high- or medium-throughput experimental campaigns to be vastly more efficient. You will execute small batches of experiments in your laboratories, and PDT's predictive models will quickly analyze your data and determine which experiments you should do in the next iteration to move towards optimal results. This loop runs seamlessly until the desired targets are found.
PDT is unique because it offers: