Identification of Isosteric Replacements of Glycosyl Domain of Ligands by Data Mining

Abstract

Biologically equivalent replacements of key moieties in molecule rationalizes scaffold hopping, patent busting or R-group enumeration, yet heavily depending upon the expert-defined space therefore is subjective and might be biased to the chemistries they get used to. Most importantly, these explorations are often informatively incomplete since it is often confined within try-and-error cycle, only meaning what kind of substructures are suitable for the replacement occur, but fail to disclose the driving forces to support such interchanges. The Protein Data Bank (PDB) repository involving receptor-ligand interactional information reminds poorly exploited. However, manual screening the PDB become almost impossible to excavate the bioisosteric know-how with the exponentially increase of data. Therefore, a textual content parsing workflow is developed to automatedly mine local structural replacement (LSR) of specific structure. Taking the glycosyl domain for instance, a total of 41652 replacements that overlap on nucleotide ribose were identified and categorized based on their SMILE codes. Predominately ring system, such as aliphatic aromatic ring, yet amide and sulfonamide replacement also occurred. We believe these findings may enlighten medicinal chemists to design and optimize ligand structure using bioisosteric replacement strategy.

DeepBioisostere: Deep Learning-based Bioisosteric Replacements for Optimization of Multiple Molecular Properties

“…[9][10][11] Traditional bioisosteric replacements heavily relied on the intuition and experience of medicinal chemists. [12] Identification of proper bioisosteric replacements has required extensive trial and error, leading to the rise of in silico approaches. [13,14] There have been two mainstream conventional approaches to finding bioisosteressimilarity-based and database mining methods.…”