Quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) are established techniques to relate endpoints to molecular features. We present the Alvascience software suite that takes care of the whole QSAR/QSPR workflow necessary to use models to predict endpoints for untested molecules. The first step, data curation, is covered by alvaMolecule. Features such as molecular descriptors and fingerprints are generated by using alvaDesc. Models are built and validated with alvaModel. The models can then be deployed and used on new molecules by using alvaRunner. We use these software tools on a real case scenario to predict the blood–brain barrier (BBB) permeability. The resulting predictive models have accuracy equal or greater than 0.8. The models are bundled in an alvaRunner project available on the Alvascience website.
AlvaBuilder is a software tool for de novo molecular
design and
can be used to generate novel molecules having desirable characteristics.
Such characteristics can be defined using a simple step by step graphical
interface, and they can be based on molecular descriptors, on predictions
of QSAR/QSPR models, and on matching molecular fragments or used to
design compounds similar to a given one. The molecules generated are
always syntactically valid since they are composed by combining fragments
of molecules taken from a training data set chosen by the user. In
this paper, we demonstrate how the software can be used to design
new compounds for a defined case study. AlvaBuilder is available at
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