# PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning
## 1 Introduction  
Modern experimental advances have enabled scientists to analyze molecular properties, and to design completely new molecules with beneficial functions [1; 2; 3; 4]. For example, the antibiotic penicillin was first discovered as a natural product in the mold _Penicillium rubens_, but subsequent drug design produced the stronger, faster-acting, and broader-spectrum antibiotic amoxicillin [5]. Furthermore, not limited to only small molecules, protein design for therapeutic applications has recently grown by leaps and bounds with the introduction of deep neural networks for structure prediction [6; 7] and sequence design [8]. SkyCovione [9], the first computationally designed nanoparticle vaccine developed for COVID-19, elicits a superior immune response than existing mRNA vaccines, and has been approved for use in South Korea to fight COVID-19. Drugs such as amoxicillin and SkyCovione could not save lives today were it not for careful characterization of experimental data. From large datasets, analytical measurements (\"observables\") are refined into more concise \"features\"--specific subsets that capture relevant properties of the sample. For example, features describing amoxicillin might include its solubility, its bioavailability, the setof endogenous proteins it is known to bind to (so-called \"promiscuous\" drugs can bind to multiple receptors [10], see below), and the crystal structures of these bound complexes. Similarly, the SkyCovione nanoparticle may be described by its elicited antibody titer, its retention volume distribution by size-exclusion chromatography, or the kinetic association constant between its domains and the SARS-CoV-2 spike. These data are most often generated from various experimental assays and screening protocols, and reliable data gathering requires the skills necessary to prepare the sample and operate the instrument. Designing features based on the resulting data is often challenging as the high level of expertise required to accurately interpret the experimental results is often exclusive to the expertise required for feature design. Cursory instrument operation, incomplete knowledge of experimental protocols, and improper analysis can lead to erroneous features because each step can introduce a distinct form of error. These errors are discussed in Section 2.3.3. Promiscuity refers to the behavior of a given ligand or receptor to bind to multiple receptors or ligands, respectively, each with a high affinity (distinguished by whether the kinetic dissociation constant of the protein-ligand complex is less than 10 uM). Promiscuous drugs are usually small and hydrophobic (which reduces the required specificity of interaction imposed by binding site electrostatics and hydrogen bonding, while retaining interaction strength) and promiscuous binding sites are able to accommodate diverse ligands through complementary hydrophobicity and dynamics [11]. Promiscuity may be a desired function of a designed drug or protein in some applications, but it may also manifest as undesired side effects; therefore, the careful accounting of promiscuity effects is encouraged. Recent work has investigated the structure-function relationship of promiscuity [12; 13; 14], and a comprehensive toolkit to analyze and optimize promiscuity effects would be immensely helpful, but one has not, to our knowledge, been published. Many experimental assays will yield nonuseful or redundant information for the study of interest, so scientists routinely judge whether the features they design and refine contribute meaningfully to analysis--indeed, this process is analogous to the scientific method itself (Figure 1). Often new features (as well as new hypotheses) are needed to model a biochemical phenotype, so they are developed through feature engineering. In the machine learning field where the choice of input data is important [15], successful network training is inextricably linked to feature choice.