Fuzzy Tricentric Pharmacophore Fingerprints. 2. Application of Topological Fuzzy Pharmacophore Triplets in Quantitative Structure−Activity Relationships

Topological fuzzy pharmacophore triplets (2D-FPT), using the number of interposed bonds to measure
separation between the atoms representing pharmacophore types, were employed to establish and validate
quantitative structure−activity relationships (QSAR). Thirteen data sets for which state-of-the-art QSAR
models were reported in literature were revisited in order to benchmark 2D-FPT biological activity-explaining
propensities. Linear and nonlinear QSAR models were constructed for each compound series (following
the original author's splitting into training/validation subsets) with three different 2D-FPT versions, using
the genetic algorithm-driven Stochastic QSAR sampler (SQS) to pick relevant triplets and fit their coefficients.
2D-FPT QSARs are computationally cheap, interpretable, and perform well in benchmarking. In a majority
of cases (10/13), default 2D-FPT models validated better than or as well as the best among those reported,
including 3D overlay-dependent approaches. Most of the analogues series, either unaffected by protonation
equilibria or unambiguously adopting expected protonation states, were equally well described by rule- or
pKa-based pharmacophore flagging. Thermolysin inhibitors represent a notable exception:  pKa-based flagging
boosts model quality, althoughsurprisinglynot due to proteolytic equilibrium effects. The optimal degree
of 2D-FPT fuzziness is compound set dependent. This work further confirmed the higher robustness of
nonlinear over linear SQS models. In spite of the wealth of studied sets, benchmarking is nevertheless
flawed by low intraset diversity:  a whole series of thereby caused artifacts were evidenced, implicitly raising
questions about the way QSAR studies are conducted nowadays. An in-depth investigation of thrombin
inhibition models revealed that some of the selected triplets make sense (one of these stands for a topological
pharmacophore covering the P1 and P2 binding pockets). Nevertheless, equations were either unable to predict
the activity of the structurally different ligands or tended to indiscriminately predict any compound outside
the training family to be active. 2D-FPT QSARs do however not depend on any common scaffold required
for molecule superimposition and may in principle be trained on hand of diverse sets, which is a must in
order to obtain widely applicable models. Adding (assumed) inactives of various families for training enabled
discovery of models that specifically recognize the structurally different actives.