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Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by increased pulmonary arterial pressure and pulmonary vascular resistance, which result in an increase in afterload imposed onto the right ventricle, leading to right heart failure. Current therapies are incapable of reversing the disease progression. Thus, the identification of novel and potential therapeutic targets is urgently needed. An alteration of nucleotide- and nucleoside-activated purinergic signaling has been proposed as a potential contributor

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To address this issue, we extend our approach to a Δ-learning scheme, where the ML model learns the difference between a reference method (density functional theory (DFT)) and a cheaper semiempirical method (density functional tight binding (DFT). We show that such a scheme reaches the accuracy of the DFT reference method while requiring significantly less parameters. Furthermore, the Δ-learning scheme is capable of correctly incorporating long-range interactions within a cutoff of 1.4 nm. It is validated by performing MD