Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints

We herewith present a novel approach to predict protein–ligand binding modes from the single two-dimensional structure of the ligand. Known protein–ligand X-ray structures were converted into binary bit strings encoding protein–ligand interactions. An artificial neural network was then set up to first learn and then predict protein–ligand interaction fingerprints from simple ligand descriptors. Specific models were constructed for three targets (CDK2, p38-α, HSP90-α) and 146 ligands for which protein–ligand X-ray structures are available. These models were able to predict protein–ligand interaction fingerprints and to discriminate important features from minor interactions. Predicted interaction fingerprints were successfully used as descriptors to discriminate true ligands from decoys by virtual screening. In some but not all cases, the predicted interaction fingerprints furthermore enable to efficiently rerank cross-docking poses and prioritize the best possible docking solutions

Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints

We herewith present a novel approach to predict protein–ligand binding modes from the single two-dimensional structure of the ligand. Known protein–ligand X-ray structures were converted into binary bit strings encoding protein–ligand interactions. An artificial neural network was then set up to first learn and then predict protein–ligand interaction fingerprints from simple ligand descriptors. Specific models were constructed for three targets (CDK2, p38-α, HSP90-α) and 146 ligands for which protein–ligand X-ray structures are available. These models were able to predict protein–ligand interaction fingerprints and to discriminate important features from minor interactions. Predicted interaction fingerprints were successfully used as descriptors to discriminate true ligands from decoys by virtual screening. In some but not all cases, the predicted interaction fingerprints furthermore enable to efficiently rerank cross-docking poses and prioritize the best possible docking solutions

Discovery of <i>N</i>‑(Pyridin-4-yl)-1,5-naphthyridin-2-amines as Potential Tau Pathology PET Tracers for Alzheimer’s Disease

A mini-HTS on 4000 compounds selected using 2D fragment-based similarity and 3D pharmacophoric and shape similarity to known selective tau aggregate binders identified <i>N</i>-(6-methylpyridin-2-yl)­quinolin-2-amine <b>10</b> as a novel potent binder to human AD aggregated tau with modest selectivity versus aggregated β-amyloid (Aβ). Initial medicinal chemistry efforts identified key elements for potency and selectivity, as well as suitable positions for radiofluorination, leading to a first generation of fluoroalkyl-substituted quinoline tau binding ligands with suboptimal physicochemical properties. Further optimization toward a more optimal pharmacokinetic profile led to the discovery of 1,5-naphthyridine <b>75</b>, a potent and selective tau aggregate binder with potential as a tau PET tracer