Discovery of high‐affinity amyloid ligands using a ligand‐based virtual screening pipeline

Abstract

AbstractBackgroundA ligand‐based virtual screening pipeline was developed to discover structurally novel ligands for Aβ(1‐42) fibrils.MethodA database of ligands that bind to Aβ(1‐42) fibrils was used to develop a two‐step ligand‐based virtual screening pipeline. The first step involved developing machine learnings models that use relatively simple chemical descriptors to predict ligand dissociation constants (Kd ). The second step involved constructing 3D models based on field points that describe the surface, shape, and electronic properties of ligands. The combined pipeline was then used to screen 63 million compounds from the ZINC15 database.1 The most promising compounds were experimentally evaluated using binding assays.ResultFor the first chemical descriptor model, a support vector machine model was found to predict the ‐log(Kd /M) of Aβ(1‐42) fibril ligands with a mean absolute error of only 0.41. An ensemble of four 3D models were then developed that each predicted ‐log(Kd /M) with a cross‐validation regression coefficient of 0.48–0.56. The ZINC15 database compounds were screened through the first chemical descriptor model, and the 10,000 compounds with the highest predicted affinities were then screened through the 3D models. The 46 highest‐ranked ligands were then evaluated using Thioflavin T competition assays, resulting in five new Aβ(1‐42) ligands with novel structures that exhibited nanomolar binding.ConclusionA ligand‐based virtual screening pipeline has been developed to discover new fibril‐binding ligands. Using Aβ(1‐42) fibrils as a target, five new structurally diverse ligands were found that exhibit nanomolar binding affinities.1. Sterling and J. J. Irwin, J. Chem. Inf. Model. 2015, 55, 2324‐2337.