AlphaFold Models Enhance Drug Discovery: Expanding Exploitable Targets and Structure-Oriented Approaches

The availability of new AlphaFold models is a significant boost to drug design, as it increases the number of exploitable targets and drives progress towards more structure-oriented drug discovery [15].

In this research, all reverse dockings were performed using Schr￶dinger's Glide (version 6.7; Schr￶dinger Inc.) [12, 13]. Ligand compounds were processed using the LigPrep module in the Maestro tool to generate low-energy three-dimensional structures. Protein structures were processed in batches by the Protein Preparation Wizard module, with default parameters including dehydrating, hydrocharging, and complementing the side chain. The binding sites on proteins were identified using the SiteMap module to generate a receptor grid, and docking boxes were generated using the Grid module with standard precision SP mode of Glide. Docking scores were based on a combination of van der Waals energy, coulomb energy, lipophilic term, hydrogen bond term, metal bond term, reward, and punishment. The process was automated on a Linux system through Python script calls to each module.

Target proteins were screened for their mechanism of action based on a gscore < -8 kcal/mol against the drugbank target database. We also screened proteins associated with side effects and toxicity based on gscore < -8 kcal/mol and available data from the clinical chemistry literature. These proteins are involved in important cellular metabolic processes such as amino acid and nucleotide metabolism, glycolytic pathways, and the urea cycle, which may lead to adverse effects.