首页|University of Maryland Reports Findings in Machine Learning (Combined Physics- a nd Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS -Hotspots)
University of Maryland Reports Findings in Machine Learning (Combined Physics- a nd Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS -Hotspots)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting out of Baltimore, Maryland, b y NewsRx editors, research stated, “Identifying druggable binding sites on prote ins is an important and challenging problem, particularly for cryptic, allosteri c binding sites that may not be obvious from X-ray, cryo-EM, or predicted struct ures. The Site-Identification by Ligand Competitive Saturation (SILCS) method ac counts for the flexibility of the target protein using all-atom molecular simula tions that include various small molecule solutes in aqueous solution.” Our news journalists obtained a quote from the research from the University of M aryland, “During the simulations, the combination of protein flexibility and com prehensive sampling of the water and solute spatial distributions can identify b uried binding pockets absent in experimentally determined structures. Previously , we reported a method for leveraging the information in the SILCS sampling to i dentify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the H otspots according to the likelihood they can accommodate drug-like molecules (e. g., molecular weight >200 Da). In the independent valida tion set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the to p 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model’s output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets.”
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