Quantitative Estimate of Protein-Protein Interaction Targeting Drug-likeness

Abstract

The quantification of drug-likeness is very useful for screening drug candidates. The quantitative estimate of drug-likeness (QED) is the most commonly used quantitative drug efficacy assessment method proposed by Bickerton <i>et al</i>. However, QED is not considered suitable for screening compounds that target protein-protein interactions (PPI), which have garnered significant interest in recent years. Therefore, we developed a method called the quantitative estimate of protein-protein interaction targeting drug-likeness (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs and developed using the QED concept, involving modeling physicochemical properties based on the information available on the drug. QEPPI models the physicochemical properties of compounds that have been reported in the literature to act on PPIs. Compounds in iPPI-DB, which comprises PPI inhibitors and stabilizers, and FDA-approved drugs were evaluated using QEPPI. The results showed that QEPPI is more suitable for the early screening of PPI-targeting compounds than QED. QEPPI was also considered an extended concept of "Rules of Four" (RO4), a PPI inhibitor index proposed by Morelli <i>et al</i>. To compare the discriminatory performance of QEPPI and RO4, we evaluated their discriminatory performance using the datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. Results of the F-score of RO4 and QEPPI were 0.446 and 0.499, respectively. QEPPI demonstrated better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it could be used as an initial filter for efficient screening of PPI-targeting compounds, which has been difficult in the past.<br>

Transformer-Based Molecular Generative Model for Antiviral Drug Design

“…45 After training and generating 30 000 molecules, we calculated these properties including molecular weight, natural products likeness (NPlikeness), LogP, SAscore, QED, and quantitative estimate of protein−protein interaction targeting drug-likeness (QEPPI). 13,49 It is noteworthy that both C5T5 and our model are conditional generation models. It means that the properties distribution of generated molecules is decided to be the input sequence and the condition set; instead, the other two models can sample without input.…”