Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer aided molecular design II: experimental and theoretical assessment of a novel method for virtual screening of fasciolicides.

A novel method for in silico selection of fluckicidal drugs is introduced. Two QSARs that permit us to discriminate between fasciolicide and non-fasciolicide drugs (the first) and to outline some conclusions about the possible mechanism of action of a chemical (the second) are performed. The first model correctly classified 93.85% of compounds in the training series and 89.5% of the compounds in the predicting one. This model correctly classified 87.7, 93.8, 92.2 and 93.9% of compounds in leave- n-out cross validation procedures when n takes values from 2 to until 6. The model seems to be stable in around 92% of good classification in leave- n-out cross validation analysis when n>6. The second model correctly classified 70% of non-fasciolicide compounds, 85.71% of beta-tubulin inhibitors and 100% of proton ionophores in the training set. This model recognizes as proton ionophores 100% of any nitrosalicylanilides in the predicting series. Both models have a low p-level <0.05. Finally, the experimental assay of six organic chemicals by an in vivo test permit us to carry out an assessment of the model with a fairly good 100% agreement between experiment and theoretical prediction.