Combined Machine Learning and GRID-Independent Molecular Descriptor (GRIND) Models to Probe the Activity Profiles of 5-Lipoxygenase Activating Protein Inhibitors - Khan, Hafiza Aliza; Jabeen, Ishrat (Frontiers in Pharmacology, 2022)
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 LTs production at the earliest level providing relief to patients requiring anti-leukotriene therapy. Over the last three 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 structural requirement of FLAP modulators. Here in this study, supervised machine learning techniques and molecular modelling 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 for revelation of 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 most probable binding poses from docking studies, GRIND model was developed which indicated the positive contribution of four hydrophobic, two hydrogen bond acceptors, and two shape-based features at certain distances from each other towards inhibitory potency of FLAP modulators. Collectively, this study sheds light on important two-dimensional and three-dimensional structural requirements of FLAP modulators which can potentially guide the development of more potent chemotypes for the treatment of inflammatory disorders.