In this work, the SOFT.PTML tool has been used to pre-process
a
ChEMBL dataset of pre-clinical assays of antileishmanial compound
candidates. A comparative study of different ML algorithms, such as
logistic regression (LOGR), support vector machine (SVM), and random
forests (RF), has shown that the IFPTML-LOGR model presents excellent
values of specificity and sensitivity (81–98%) in training
and validation series. The use of this software has been illustrated
with a practical case study focused on a series of 28 derivatives
of 2-acylpyrroles 5a,b, obtained through
a Pd(II)-catalyzed C–H radical acylation of pyrroles. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated finding that compounds 5bc (IC50 = 30.87 μM, SI > 10.17) and 5bd (IC50 = 16.87 μM, SI > 10.67) were approximately
6-fold more selective than the drug of reference (miltefosine) in in vitro assays against L. amazonensis promastigotes. In addition, most of the compounds showed low cytotoxicity,
CC50 > 100 μg/mL in J774 cells. Interestingly,
the
IFPMTL-LOGR model predicts correctly the relative biological activity
of these series of acylpyrroles. A computational high-throughput screening
(cHTS) study of 2-acylpyrroles 5a,b has
been performed calculating >20,700 activity scores vs a large space of 647 assays involving multiple Leishmania species, cell lines, and potential target proteins. Overall, the
study demonstrates that the SOFT.PTML all-in-one strategy is useful
to obtain IFPTML models in a friendly interface making the work easier
and faster than before. The present work also points to 2-acylpyrroles
as new lead compounds worthy of further optimization as antileishmanial
hits.