- Poster presentation
- Open Access
Additive inductive learning in QSAR/QSPR studies and molecular modeling
© Baskin et al; licensee BioMed Central Ltd. 2009
- Published: 05 June 2009
where x i is the value of the i-th descriptor, c j – is the values of the j-th learnable (adjustable) parameter, f – a non-linear function. Parameters c j are learned using some training set of compounds.
where z ij is the value of the j-th descriptor for the i-th subobject. This definition can be extended to include several types of subobjects.
Due to the existence of many additive properties in chemistry and physics (the energy is the most prominent one), AIL is especially well suited for solving numerous problems in chemo- and bioinformatics, ranging from QSAR/QSPR studies up to molecular modeling, computing binding energies, etc.
where K ab is the kernel for the pair of objects a and b, while k(a, i, b, j) is the "microkernel" for the pair of the i-th subobject in a and the j-th subobject in b.
Several examples of using AIL in chemoinformatics and the prospects of its application in molecular modeling are considered.
This article is published under license to BioMed Central Ltd.