## 4 Conclusions
\begin{tabular}{l l} \(C\) & Cluster subgraphs \\ \(CC_{Active}\) & Number of molecules correctly classified as active \\ \(CC_{Inactive}\) & Number of molecules correctly classified as inactive \\ \(Ch\) & Cycle (ring) subgraphs \\ \(CLC\)-Pred & Cell line cytotoxicity predictor \\ \(ct\) & Cellular target (type of lung cancer cell line) \\ \(D[GBI]_{Ej}\) & Multi-label graph indices calculated from the chemical structures and a defined \\  & element of _ej_ (_ct_ or _mb_) \\ \(e(SG)m\) & Edge (bond-based) connectivity indices of order \(m\) involving subgraphs _SG_ \\ \(ej\) & Experimental condition depending on the elements _ct_ and _mb_ \\ \(FBTD\) & Fragment-based topological design \\ \(GBI\) & Graph-based indices \\ \(\mathrm{GI}_{50}\) & Concentration of a compound that reduced total cell growth by 50\% \\ GR & Gain ratio \\ \(H\) & Number of hidden neurons \\ \(Hyd\) & Hydrophobic contribution \\ \(I\) & Number of input nodes \\ \(k\) & Maximum number of bonds present in a fragment without counting bond multiplicity \\ logP & Logarithm of the octanol/water partition coefficient \\ \(n\)(ct) & Number of molecules in the training set labeled as active and experimentally \\ \(m\) & Exact number of bonds present in a fragment without counting bond multiplicity \\ \(mb\) & Mutability of a lung cancer cell line \\ \(MCC\) & [PERSON]'s correlation coefficient \\ MlogP & Logarithm of the octanol-water partition coefficient according to [PERSON]'s method \\ MLP & Multilayer perceptron \\ \(Mol\) & Molar refractivity contribution \\ MW & Molecular weight \\ \(n\)(ct) & Number of molecules in the training set labeled as active and experimentally \\  & assayed against the same element lung cancer cell line (_ct_) \\ \(n\)(ej) & Number of molecules in the training set labeled as active and experimentally \\  & assayed against the same element of _ej_ (_ct_ or _mb_) \\ \(N\)(ej) & Number of molecules in the training set that have been experimentally \\  & assayed against the same element of _ej_ (_ct_ or _mb_) \\ \(n\)(mb) & Number of molecules in the training set labeled as active and \\  & experimentally assayed against the same element lung cancer cell line (_ct_) \\ \(N_{Active}\) & Number of molecules labeled as active \\ \(NB\) & Number of bonds in a molecule without counting bond multiplicity \\ NHBA & Number of hydrogen bond acceptors \\ NHBD & Number of hydrogen bond donors \\ \(N_{Inactive}\) & Number of molecules annotated as inactive \\ NRB & Number of rotatable bonds \\ NTD & Size-independent topological descriptors \\ \(O\) & Number of output nodes \\ \(P\) & Path subgraphs \\ \(Pa\) & Probability calculated by CLC-Pred 2.0 for a molecule to be classified as active \\ \(PC\) & Path-cluster subgraphs \\ \(PCC\) & [PERSON]'s correlation coefficient \\ \(P_{i}\) & Probability calculated by CLC-Pred 2.0 for a molecule to be classified as inactive \\ \(PP\) & Physicochemical property \\ \(Pred[ALC(ej)]\) & Predicted categorical value of the anticancer activity ALC(_ej_) \\ \(ps(ej)\) & Probabilistic factor \\ \(Psa\) & Polar surface area \\ PTML & Perturbation theory machine learning \\ PTML-MLP & Perturbation theory machine learning based on a multilayer perceptron \\ QSAR & Quantitative structure-activity relationships \\ \(Sdv[GBI]\) & Standard deviation calculated for values from each \(GBI\) descriptor \\ \(SG\) & Subgraph/generic fragment \\ \(SM(PP)k\) & Bond-based spectral moments of order \(k\) weighted by the property PP \\ \end{tabular}