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We report a novel small-molecule screening approach that combines data augmentation and machine learning to identify Food and Drug Administration (FDA)-approved drugs interacting with the calcium pump (Sarcoplasmic reticulum Ca2+- ATPase, SERCA) from skeletal (SERCA1a) and cardiac (SERCA2a) muscle. This approach uses information about smallmolecule effectors to map and probe the chemical space of pharmacological targets, thus allowing to screen with high precision large databases of small molecules, including approved and investigational drugs. We chose SERCA because it plays a major role in the excitation-contraction-relaxation cycle in muscle and it represents a major target in both skeletal and cardiac muscle. The machine learning model predicted that SERCA1a and SERCA2a are pharmacological targets for seven statins, a group of FDA-approved 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors used in the clinic as lipid-lowering medications. We validated the machine learning predictions by using in vitro ATPase assays to show that several FDA-approved statins are partial inhibitors of SERCA1a and SERCA2a. Complementary atomistic simulations predict that these drugs bind to two different allosteric sites of the pump. Our findings suggest that SERCA-mediated Ca2+ transport may be targeted by some statins (e.g., atorvastatin), thus providing a molecular pathway to explain statinassociated toxicity reported in the literature. These studies show the applicability of data augmentation and machine learning-based screening as a general platform for the identification of off-target interactions and the applicability of this approach extends to drug discovery. [ABSTRACT FROM AUTHOR]

A key molecular dysfunction in heart failure is the reduced activity of the cardiac sarcoplasmic reticulum Ca 2+ -ATPase (SERCA2a) in cardiac muscle cells. Reactivating SERCA2a improves cardiac function in heart failure models, making it a validated target and an attractive therapeutic approach for heart failure therapy. However, finding small-molecule SERCA2a activators is challenging. In this study, we used a machine learning-based virtual screening to identify SERCA2a activators among 57,423 natural products. The machine learning model identified ten structurally related natural products from Zingiber officinale, Aframomum melegueta, Alpinia officinarum, Alpinia oxyphylla, and Capsicum (chili peppers) as SERCA2a activators. Initial ATPase assays showed seven of these activate SERCA at low micromolar concentrations. Notably, two natural products, Yakuchinone A and Alpinoid D displayed robust concentration-dependent responses in primary ATPase activity assays, efficient lipid bilayer binding and permeation in atomistic simulations, and enhanced intracellular Ca 2+ transport in adult mouse cardiac cells. While these natural products exert off-target effects on Ca 2+ signaling, these compounds offer promising avenues for the design and optimization of lead compounds. In conclusion, this study increases the array of calcium pump effectors and provides new scaffolds for the development of novel SERCA2a activators as new therapies for heart failure., (© 2025 The Authors. ChemMedChem published by Wiley-VCH GmbH.)