Combined Machine Learning and GRID-Independent Molecular Descriptor (GRIND) Models to Probe the Activity Profiles of 5-Lipoxygenase Activating Protein Inhibitors

Abstract

Leukotrienes (LTs) are pro-inflammatory lipid mediators derived from arachidonic acid (AA), and their high production has been reported in multiple allergic, autoimmune, and cardiovascular disorders. The biological synthesis of leukotrienes is instigated by transfer of AA to 5-lipoxygenase (5-LO) via the 5-lipoxygenase-activating protein (FLAP). Suppression of FLAP can inhibit LT production at the earliest level, providing relief to patients requiring anti-leukotriene therapy. Over the last 3 decades, several FLAP modulators have been synthesized and pharmacologically tested, but none of them could be able to reach the market. Therefore, it is highly desirable to unveil the structural requirement of FLAP modulators. Here, in this study, supervised machine learning techniques and molecular modeling strategies are adapted to vaticinate the important 2D and 3D anti-inflammatory properties of structurally diverse FLAP inhibitors, respectively. For this purpose, multiple machine learning classification models have been developed to reveal the most relevant 2D features. Furthermore, to probe the 3D molecular basis of interaction of diverse anti-inflammatory compounds with FLAP, molecular docking studies were executed. By using the most probable binding poses from docking studies, the GRIND model was developed, which indicated the positive contribution of four hydrophobic, two hydrogen bond acceptor, and two shape-based features at certain distances from each other towards the inhibitory potency of FLAP modulators. Collectively, this study sheds light on important two-dimensional and three-dimensional structural requirements of FLAP modulators that can potentially guide the development of more potent chemotypes for the treatment of inflammatory disorders.

Exploring QSAR of FLAP inhibitors using kernel partial least squares modeling: Insights from molecular binary fingerprints

“…This study significantly advances the field by demonstrating the efficacy of KPLS modeling with molecular binary fingerprints in capturing the complex, non-linear relationships in the QSAR of FLAP inhibitors. Previous research often combined multiple techniques, such as pharmacophore modeling, docking, QSAR, and ADMET analyses, or utilized machine learning with GRID-Independent Molecular Descriptors (GRIND) [21], [22], [23]. These methods provided robust predictions but required significant computational resources.…”

Structures of Leukotriene Biosynthetic Enzymes and Development of New Therapeutics

“…Of interest is the fact that DG-031 also inhibits the MAPEG enzyme mPGES-1, but the strikingly different binding modes suggest that further modification of DG-031 can confer MAPEG specificity (Figure 4b). A recent study used artificial intelligence and molecular dynamics simulations applied to 187 existing FLAP inhibitor structures to define 2D and 3D structural requirements of FLAP modulators (70). It is notable that a single-amino-acid difference between the mouse and human FLAP can determine the speciation and differential pharmacology of novel FLAP inhibitors (71).…”