Sentence examples for forecast interaction from inspiring English sources

The present study describes the development of a robust homology model of LdSHMT to forecast interaction phenomenon with inhibitory molecules using structure-based drug designing strategy.

The present study describes development of robust homology model of Leishmania donovani adenosine kinase to forecast interaction phenomenon with inhibitory molecules using structure-based drug designing strategy.

These organizations should thus invest in forecasting interaction effects in ex ante assessment, and ensure that such effects are adequately taken into account in ex post analysis.

However, as demonstrated in this study, molecular docking represents a fast and reliable cheminformatics technique to forecast drug HLA interactions.

Performing molecular docking using the crystals 3VRI, 3VRJ, and 3UPR should thus be able to forecast meaningful interactions between HLA-B*57 01 HLA-B*57 01rom the test set.

As a principal engineer and futurist at the world's largest chip manufacturer, Intel, he forecasts the interaction between humans and computers, using insights from a variety of scientific fields, to help the company's engineers and product developers.

In the absence of extensive HLA-related chemogenomics data in the public domain, the development of virtual screening models that can accurately forecast drug-HLA interactions is extremely difficult.

Dynamic forecast models show the interaction of the tropical cyclone with its environment, but they require the use of large and powerful computers as well as very complete descriptions of the structure of the tropical cyclone and that of the surrounding environment.

Therefore, computational approaches able to forecast such HLA drug molecular interactions reliably could have serious implications in preventing ADRs and thus potentially contribute to the development of precision medicine.

The most useful numerical lava flow models are those that are both accurate and efficient, and have the physical complexity necessary for applications ranging from simple flow path prediction to real-time flow forecasting to thermal interactions with the environment.

The forecasting activity results from the interaction of a population of experts, each integrating genetic and neural technologies.