# Cocrystal Prediction Using Machine Learning Models and Descriptors
## 4 Discussion  
Although the models performed best when we use all features, the feature selection algorithms showed their potential using only half of the features (e.g., \(F_{S}=1103\) or \(900\)) gave comparable results. One might want to see what features were valuable than others. Table 8 shows lists of best and worst features obtained by K-best algorithm with \(F_{S}=900\), where the scores are ANOVA F-values; the features with larger scores turn out to be more vital than others. Note that the features are grouped in terms of _Module_; for example, the best feature 'Mp' came from 'Constitutional' module of [PERSON]. Interestingly, many of the best and worst features commonly came together from the 'Autocorrelation' module, which computes the Moreau-Broto autocorrelation of the topological structure. Most of the best features of the 'Autocorrelation' module are Geary coefficients (e.g., 'GATS' series), implying that the spatial correlation is particularly essential in predict cocrystal formation. Especially, Geary coefficients weighted by intrinsic state (e.g., GATS4s, GATS6s), valence electrons (e.g., GATS6 dv), or atomic number (e.g., GATS3Z, GATS6Z, GATS7Z, GATS8Z) turned out to be extremely important. On the other hand, most of the worst features of the 'Autocorrelation' module, are the Moran coefficient (e.g., MATS series). The Moran coefficient focuses on deviations from the mean whereas, the Geary coefficient focuses on the deviations of individual observation area [61], so we might say that the deviations of each observation area are more meaningful information for cocrystal prediction. It is consistent with a recent study by [PERSON] et al. [62] that revealed that the Moran coefficient is not very competent in detecting spatial patterns other than simple autocorrelation due to its asymptotic normality for p-value computation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**30 Best Features**} & \multicolumn{4}{c}{**30 Worst Features**} \\ \hline
**Module** & **Name** & **Score** & **Module** & **Name** & **Score** \\ \hline \multirow{9}{*}{Autocorrelation} & GATS3c & 79.718 & Aromatic & nAromAtom & 0.136 \\  & GATS6c & 85.965 & & AATS7m & 0.628 \\  & GATS8c & 136.802 & & ATSC7p & 0.007 \\  & GATS6 dv & 161.104 & & AATSC8s & 0.016 \\  & GATS4s & 172.971 & & AATSC5 are & 0.017 \\  & GATS6s & 175.205 & & MATS7 dv & 0.521 \\  & GATS3Z & 80.696 & & MATS5s & 0.027 \\  & GATS6Z & 81.969 & & MATS7s & 0.039 \\  & GATS7Z & 84.171 & & MATS8Z & 0.195 \\  & GATS8Z & 84.774 & & MATS3w & 0.357 \\  & ATS2m & 86.768 & & MATS3 se & 0.001 \\  & MATSare & 90.363 & & MATS5 pe & 0.390 \\ \hline \multirow{2}{*}{AdjacencyMatrix} & SpDiam\_A & 85.488 & & MATS6p & 0.477 \\  & VE3\_A & 78.929 & & GATS1c & 0.322 \\  & VE1\_A & 77.862 & & GATS6d & 0.117 \\ \hline Chi & AXp-7 dv & 82.701 & & GATS3s & 0.469 \\ \hline \multirow{2}{*}{Constitutional} & SZ & 79.156 & & GATS1i & 0.004 \\  & Mare & 82.745 & & GATS5i & 0.264 \\  & Mp & 79.163 & & GATS7i & 0.166 \\ \hline \multirow{2}{*}{DetourMatrix} & DetourIndex & 80.610 & & GATS8i & 0.004 \\ \hline \multirow{9}{*}{ESState} & NsssP & 81.691 & & BCUTd-1h & 0.474 \\  & NdssP & 78.913 & & BCUTd-1l & 0.361 \\  & NsBr & 81.782 & & BCUTv-1l & 0.584 \\  & SssNH & 81.836 & & BCUTp-1h & 0.019 \\  & SaaNH & 79.010 & & BCUTv-1l & 0.004 \\ \cline{2-5}  & SssssGe & 82.422 & & BaryszMatrix & SpAD\_D2Z & 0.213 \\ \cline{2-5}  & SAsAsH2 & 79.068 & & & \\ \cline{2-5}  & MINssPH & 91.111 & & & \\ \cline{2-5}  & NssSnH2 & 78.956 & & MIC3 & 0.240 \\ \hline ExtendedTopochemicalAtom & ETA\_epsilon\_5 & 78.193 & MoRSE & Mor06p & 0.303 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Best & worst features selected by K-best algorithm with \(F_{S}=900\). Many of the best features came from the 'EState' module, which generates atom type e-state descriptor values [63]. This implies that the electrostatic interaction of atoms and their topological environment (connections) within a molecule has a significant impact on cocrystallization.