Expectations from a good QSAR tool in
Drug Discovery Applications, December 2008
While the above modules and their desired functions describe the
overall expectations of a good QSAR software system, the following
description attempts to capture the specific requirements of each
application area of QSAR in drug discovery research.
Application #1: Compound Library Design.
When designing target focused compound libraries, QSAR models are
helpful in assessing drug-likeness of compounds using appropriate
global ADME and toxicity models. At the same time, local QSAR models
built to be sensitive to the bioisosteric transformations around
scaffolds of known activity can be helpful in designing novel
bio-equivalent compounds.
The features of QSAR software that are of most interest at this stage of discovery are:
- Global ADME and toxicity QSAR models; models based on human data from known drugs may be more valuable.
- Structure (Tanimoto) and descriptor space similarity searches
- Interpretable set of descriptors that are sensitive to R-group changes.
- Features that allow automated R-group enumerations.
Application #2: Virtual Screening
The value in assessing ADME/toxicity liabilities of compounds as early
as virtual screening has been well established. Predictions from robust
global ADME and toxicity models in addition to HTS results can be
valuable in decision support for selecting the hit series. Results from
early HTS runs can be used to build QSAR
models, and such models can then be used to profile compound
repositories and optimize the number of compounds that need to be
synthesized and/or covered in the subsequent runs. Availability of
global models based on legacy in vitro assay data would be useful in
rank ordering hits as they are lined up for in vitro studies.
The features of QSAR software that are of most interest at this stage of discovery are:
- Global ADME and toxicity QSAR models; models based on data from standardized in vitro assays.
- Local models based on “first-wave” of HTS runs.
Application #3: Lead Optimization
A large number of iterations of design, synthesis, and testing cycles
characterize the lead optimization stage of drug design. By this stage,
significant amount of assay data on the chemical series to which the
leads belong would be available. This presents an opportunity to build
a variety of QSAR models from this data and make them available to
medicinal chemists.
Lead optimization is essentially an optimization of the chemical
structure over multiple parameter dimensions. Features that allow
chemists to study the affect of structure changes on these dimensions,
in real-time, through predictions from relevant QSAR models can be
valuable in reducing the number of iterations required to converge on
optimal candidates.
The features of QSAR software that are of most interest at this stage of discovery are:
- Activity, ADME and toxicity QSAR models; models based on data from standardized in vitro and in vivo assays.
- An easy-to-use interface that allows editing
structures interactively and computing QSAR predictions on the fly.
- A function that allows the chemists to define the
optimization functions by providing relative weights for properties and
also metrics of desirability
for each property. This will allow the chemist to
simultaneously examine a large collection of compounds against a single
function value.
Closing remarks:
Despite the criticism about the effectiveness of QSAR methods for drug
discovery, most large pharmaceutical companies are employing these
methods for the type of applications discussed here. A simple fact is
that there is new data being generated every day in addition to the
mountain of data already available, and methods like QSAR are very
powerful in mining and modeling this wealth of information. There is
significant work needed to the effective apply the QSAR methods –
and software products need to play a significant role in that movement.
