Data Analysis

Modern drug discovery can lead to an overwhelming amount of data. "Big Data" has come to drug discovery, as in other fields.

Interpreting this by eye using spreadsheets is often ineffective, and error-prone.

We have a variety of computational tools to help you make sense of your data, in ways that are simple to understand and which point to future directions.

The types of applications we’ve made in the past here are:

  • Study trends in correlations between in vitro and cellular data, to highlight which assays need revision
  • Rationalize large, complex structure-activity relationships (SAR’s) using models of activity coupled to proprietary methods for uncovering mechanistically-relevant trends, e.g. non-additivity
  • Study the impact of uncertainty/noise in assays in a screening funnel

Corwin Hansch
In recent work we have found that a substituent constant, Π, derived from partition coefficients, is very useful when used with the Hammett Σ function in correlating biological activity with chemical constitution."
—Corwin Hansch


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