Pushing the Ligand Efficiency Metrics: Relative Group Contribution (RGC) Model as a Helpful Strategy to Promote a Fragment "Rescue" Effect

Abstract

The ligand efficiency (LE) indexes have long been used as decision-making criteria in drug discovery and development. However, in the context of fragment-based drug design (FBDD), these metrics often exhibit a strong emphasis toward the selection of highly efficient "core" fragments for potential optimization, which are not usually considered as parts of a larger molecule with a size typical for a drug. In this study, we present a relative group contribution (RGC) model intended to predict the efficiency of a drug-sized compound in terms of its component fragments. This model could be useful not only in rapidly predicting all the possible combinations of promising fragments from an earlier hit discovery stage, but also in enabling a relatively low-LE fragment to become part of a drug-sized compound as long as it is "rescued" by other high-LE fragments.

Keywords: drug discovery; fragment library; fragment-based screening; ligand efficiency metrics; property-based design; protein-ligand interactions; structure-activity relationship.

Figure 1

Application of RGC model to a hypothetical fragment-based drug design (FBDD) campaign. (Upper) In the “standard” or classical screening approach, a fragment is selected (i.e., can be part of a final drug-size compound) depending exclusively upon their own LE. If this parameter is not equal or greater than a pre-established cut-off value, the fragment is rejected. (Lower) According to the RGC model, a fragment is selected depending on the fragments on the other positions. Based on the presence of high-LE fragments in alternative positions (illustrated by yellow boxes), a low-LE fragment may become either rescued or rapidly discarded (using the dynamic LE_δ value in both cases).

Figure 2

Preliminary comparison between ${\text{LE}}_{\text{T}}^{\text{app}}$ and LE_T for a set of 16 drug-sized literature compounds developed during FBDD studies. All compounds were elaborated using a linking strategy (on two fragments) for 10 different protein targets [/LDHA Replication Protein A (RPA) /Blood coagulation factor Xa Bcl-2 DOT1L Hsp90 /Pantothenate synthetase (PtS) Blood coagulation factor XIa CK2 BACE1 Endothiapepsin (Epn) Bcl-x_L PKM2].