Sentence examples for prediction of activity from inspiring English sources

The prediction of activity spectra for substances (PASS) software predicts the probability of biological properties of chemicals.

Prediction of Activity Spectra Analysis of the title compound, predicts anti-diabetic activity with probability to have an active value of 0.348.

Fig. 11 The TGA curve of 3. PASS (Prediction of Activity Spectra) is an online tool [46] which predicts almost 900 types of activities based on the structure of a compound.

The prediction of activity spectra for substances (PASS) software [ 16], predicted more than 300 pharmacological effects, biological and biochemical mechanisms based on the structural formula of the substance.

The prediction of activity spectra for the substances (PASS) software, which predicted that more than 300 pharmacological effects, biological and biochemical mechanisms based on the structural formula of the substance was efficiently used in this study to reveal new multitalented actions for Vitex negundo (VN) constituents.

Obtained 3D-QSAR models were used for prediction of activity of new compounds.

In benchmark calculations on sets of known active and inactive compounds, conditional probability calculations yielded reasonably accurate predictions of activity, at least comparable to current state-of the-art machine learning approaches.

Alignment-free three-dimensional quantitative structure-activity relationship models developed on the basis of GRid Independent descriptors and partial least squares regression analysis, allow feasible predictions of activity of new compounds and reveal structural requirements for optimal affinity, particularly in the case of the 5-HT2A receptor.

For mammalian toxicity, Derek reports confidence in predictions of activity, or likelihood that activity will be seen, using terms such as "probable", "plausible".

We have identified 2-aminopyridine-3-carbonitriles as a series that showed agreement between both the ligand- and structure-based predictions of activity against A1R, A2AR and PDE10A.

Taken together, these observations show that our approach could be instrumental in translating high-throughput datasets generated by vastly different technologies into consistent predictions of activity of underlying signaling pathways and key regulatory proteins.