Covalent drug design is currently an important component in drug discovery. Traditional drugs interact with their target in a reversible equilibrium while irreversible compounds increase the duration of interactions of a drug with its target by forming a covalent bond with targeted residues, and thus may offer a more effective therapeutic approach [1]. To facilitate the design of this class of compounds, computational methods can be used to help identify reactive cysteines on a target protein for covalent binding, to test various warhead groups on ligands for their potential reactivity, and to predict non-covalent contributions to binding that can facilitate compound-protein interactions important for specificity. To aid covalent drug design we extended a functional group mapping approach based on explicit solvent all-atom molecular dynamics simulations (SILCS: Site Identification by Ligand Competitive Saturation) which intrinsically considers protein flexibility, functional group and protein desolvation along with functional group-protein interactions [2]. This method has been extensively verified to be useful in many aspects of computer-aided drug discovery [3]. In the current study, we applied SILCS to study covalent ligand binding. Through docking of molecules with representative warhead groups using SILCS-Monte Carlo (SILCS-MC), reactive cysteines of tested protein systems can be correctly identified. Furthermore, a machine learning model was trained to quantify the effectiveness of various warhead groups for protein targets using metrics from SILCS as well as experimental warhead reactivity data. The ability of ranking covalent molecular binders with the same warhead group using SILCS ligand grid free energy (LGFE) ranking was also tested for multiple protein systems. The promising result, including against kinases, indicates that the SILCS method can both qualitatively and quantitatively inform covalent drug discovery.

Covalent drug design using the site-identification by ligand competitive saturation (SILCS) method

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