Hi Michael,
On Sun, Jul 5, 2015 at 2:43 PM, Michael Reutlinger <[email protected]> wrote:
>
> I would like to use a machine learning method with the AP and DP
> descriptors as described by Robert Sheridan.
>
> AP descriptors are the 'atom pair' descriptors from Carhart et al. 1985
> and I think they are already available in RDKIT.
>
Indeed, they are.
> DP 'donor−acceptor pair', called 'BP' in Kearsley et al. 1996, is a
> reduced pharmacophore version of AP.
>
> I would like to know if you think there is a straightforward way to use
> the existing AP functionality (maybe using atomInvariants) to reproduce
> the descriptor as described in Kearsley et al.?
>
Yes, if you have a way to assign integer invariants (atom types) to atoms
that correspond to the BP features described in Kearsley et al. then it
would be very straightforward to use those in the calculation. Note that
the RDKit atom pair code does require that all atoms have a type (i.e. you
can't have atoms that are ignored like some pharmacophore methods would
do), but looking at the paper it seems like this isn't a problem: any atom
that doesn't get assigned to one of the other classes just gets put into
class 7: "other".
The paper does not, unfortunately, include enough information to directly
implement the fingerprint: you will need to come up with definitions
(probably SMARTS-based?) for the the 6 atom classes. I've thought several
times about adding the fingerprint-types from the paper to the RDKit (and
then testing them out using Sereina's benchmarking platform), but this has
always ended up getting hung-up on the missing atom-type definitions.
-greg
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