Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.

Objectives: A quantitative structure–activity relationship study using a database of 395 compounds previously tested against chloroquine-susceptible strains of the blood stages of Plasmodium falciparum to predict new in vitro antimalarial drugs has been developed. Methods: Topological indices were used as structural descriptors and were related to antimalarial activity by using linear discriminant analysis (LDA) and multilinear regression (MLR). Two discriminant equations were obtained (FD1 and FD2), which allowed us to carry out successful classification of 90% and 80% of compounds, respectively. The IC₅₀ values of the compounds were introduced to get an MLR equation model suitable to predict their in vitro activities. Results: Using this model, a set of 27 drugs against a chloroquine-susceptible clone (3D7) of P. falciparum have been selected and evaluated in vitro. Among these drugs are monensin, nigericin, vincristine, vindesine, ethylhydrocupreine and salinomycin with in vitro IC₅₀s at nanomolar concentrations (0.3, 0.4, 2, 6, 26 and 188 nM, respectively). Other compounds such as hycanthone, amsacrine, aphidicolin, bepridil, amiodarone, ranolazine and triclocarban showed in vitro IC₅₀ values below 5 μM in the mathematical model. Conclusions: These results demonstrate the usefulness of the approach for the selection and design of new lead drugs active against P. falciparum.