## 2 In Silico Library Design for Medicinal Chemistry  
When designing or examining an existing molecular library, a researcher should be aware of important steps and drawbacks associated with virtual molecules. The following chapter addresses these issues and helps the reader to develop both a critical view on the existing libraries, as well as provide a good foundation to create his/her own virtual libraries similar to the one presented in Figure 2[43; 44; 45]. After obtaining the target structure and learning about the molecular and biological context, a researcher can proceed toward designing the virtual compound library. Pharmacokinetics and pharmacodynamics represent important aspects to consider when preparing any molecular library. This was discussed by [PERSON] et al., who postulated that several drug classes, such as antibiotics, antifungals and vitamins, among others, fell outside the Ro5, due to transporter effects [46]. The term \"metabolite-likeness\" is also used to name the approach where, instead of similarity to known drugs, the library is constructed by the comparison to known metabolites, an approach that factors in pharmacokinetics [47]. All in all, both are important in molecular library design, as compound structures are critical for their behavior in a complex biological system, where absorption, distribution, metabolism and elimination (pharmacokinetics) are at play [48]. When considering the mode of action of the compound on a relevant target, one should consider all possible interactions formed between the target and the ligand, as well as factor in these data in the library design, if possible. Key interactions from covalent, ionic, metallic to the hydrogen bonds and various dipole-dipole, as well as hydrophobic interactions, should all be considered, and the potential for their formation should be assessed in the constructed compound library. Figure 2: Workflow of an efficient library preparation for medicinal chemistry. The main goal of targeted libraries is to cover a diverse chemical space with as few compounds as possible. Due to the fast-growing availability of the chemical and biological structural data, the public bioactivity databases provide an excellent starting point [49]. The most comprehensive and curated information about molecules is freely available in the prominent PubChem [50], ChEMBL [51] and ZINC [52] databases. After assembling the initial database from various sources, the next step is to remove duplicates, so that only unique structures are processed further [49]. The choice of the chemical file format is vital, as it dictates how the obtained data can be used. The most recommended file formats for representing molecules as strings include the SMILES and InChI formats. In most cases, multiple SMILES strings can equally well represent a single molecule. The application of the canonical SMILES, which uses only a single string per molecule, is recommended to avoid duplication and filtering problems. For the spatial representation of molecules, either the Structure Data Format (SDF) or the MDL Molfile (MOL) format are the most common [53]. Online libraries can usually be downloaded in these formats, making it easier to obtain a coherent library. When performing filtering, clustering or similarity searches, the SMILES format is preferred, as it leads to faster processing, due to its string representation; however, the spatial information is required for downstream methods such as 3D pharmacophore development, molecular docking and molecular dynamics. The molecular representation always requires the extra care and exploration in terms of conformational viability, chiral centers, tautomerism, compound ionization, presence of salts, structural faults, etc. By default, the hydrogen atoms are often not present in various chemical file formats and should be added during the library preparation.