# PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning
## 5 Analysis
Thus, it is expected that rotational and translational invariance is concentrated in molecules without a structural representation. Recent work has produced architectures capable of learning from features with rotational and translational equivariance [39], such as the SE(3) transformer [40], so these issues with many structural features are not insurmountable. Fewer features are capable of representing amino acid sidechains, which correlates with a presence of global structural analyses that subselected alpha-carbon atoms from a larger protein structure, while neglecting amino acid side chains. This is useful for global structural or dynamical features, but less so for features describing molecular interactions (binding or enzymatic). However, as sidechain-scale local dynamical features are developed for faster biomolecular rearrangements, this difference should diminish (Table 1). Approximately one-quarter of features represent forces of any kind, which are likely to be concentrated in features with dynamic representations. Bond types are far more variable, with hydrogen bonds and salt bridges being most commonly represented in one-third of features, with pi bonds being least represented at fewer than one-fifth. This disparity signifies greater attention applied to hydrogen bonds, which is generally true in the field due to their known importance in secondary structure, stabilization of internal protein structure, solvent interactions, and ligand binding. By contrast, pi bonds and metal bonds are far less common. As more precise structural and dynamical methods are developed, these types of interactions should gain more attention (Table 1). Approximately half of features are capable of representing environmental features such as solvent, solute, and pockets. Trapped water-capable representations are less represented in the dataset, as they required not only the appropriate experimental structural detail but also solvent representation, among other details (Table 1). Among the mathematical properties surveyed, features are most commonly guaranteed to be valid representations. This is expected, as many analysis algorithms give deterministic output generated from extensively validated static experimental data (such as crystal structures from the PDB [41] and PDBBind [42]), while by contrast experimental methods often require specialized skills. Some computational methods are not guaranteed to be valid, such as biased sampling molecular simulations. For example, over 40% of features are of fixed dimensions or can represent detail hierarchically. The least commonly satisfied properties are subset uniqueness, which is only guaranteed with certain latent space representations from neural networks. Reversibility is often lost because many features concentrate on the most relevant parts of a dataset while ignoring other aspects of an experiment. Only one-third of features can be interpreted probabilistically, which is highly represented with dynamical and reduced structural features (Table 1). Correlated properties (Table 2) tend to fall pairwise into three groups: mathematical, interactions (including forces and bonds), and environment. These results are easily rationalized because features describing bonds tend to describe many types of bonds, rotationally invariant metrics are almost always translationally invariant, and features describing energetic detail are closely related with bonds and environment. An interesting correlation is between reversibility and subset-uniqueness. This may be rationalized because reversible transformations compress essential information into a secondary space, such that all parts of the transformed space are sufficiently unique to distinguish the elements in the untransformed space. Qualitative analysis of two-dimensional t-SNE plots of the feature database (Figure 8) revealed that dynamically capable features tend to reside in a corner of the space, the area shown with the yellow background in Figure 8, and that more dynamically capable features are also capable of representing complexes.