Drug repurposing based on a quantum-inspired method versus classical fingerprinting uncovers potential antivirals against SARS-CoV-2

The COVID-19 pandemic has accelerated the need to identify new antiviral therapeutics at pace, including through drug repurposing. We employed a Quadratic Unbounded Binary Optimization (QUBO) model, to search for compounds similar to Remdesivir, the first antiviral against SARS-CoV-2 approved for human use, using a quantum-inspired device. We modelled Remdesivir and compounds present in the DrugBank database as graphs, established the optimal parameters in our algorithm and resolved the Maximum Weighted Independent Set problem within the conflict graph generated. We also employed a traditional Tanimoto fingerprint model. The two methods yielded different lists of lead compounds, with some overlap. While GS-6620 was the top compound predicted by both models, the QUBO model predicted BMS-986094 as second best. The Tanimoto model predicted different forms of cobalamin, also known as vitamin B12. We then determined the half maximal inhibitory concentration (IC50) values in cell culture models of SARS-CoV-2 infection and assessed cytotoxicity. We also demonstrated efficacy against several variants including SARS-CoV-2 Strain England 2 (England 02/2020/407073), B.1.1.7 (Alpha), B.1.351 (Beta) and B.1.617.2 (Delta). Lastly, we employed an in vitro polymerization assay to demonstrate that these compounds directly inhibit the RNA-dependent RNA polymerase (RdRP) of SARS-CoV-2. Together, our data reveal that our QUBO model performs accurate comparisons (BMS-986094) that differed from those predicted by Tanimoto (different forms of vitamin B12); all compounds inhibited replication of SARS-CoV-2 via direct action on RdRP, with both models being useful. While Tanimoto may be employed when performing relatively small comparisons, QUBO is also accurate and may be well suited for very complex problems where computational resources may limit the number and/or complexity of possible combinations to evaluate. Our quantum-inspired screening method can therefore be employed in future searches for novel pharmacologic inhibitors, thus providing an approach for accelerating drug deployment.Author summary: Drug repurposing has emerged as one key strategy in the rapid development of treatments against SARS-CoV-2 infection. Remdesivir (RDV) was the first antiviral approved for human use against SARS-CoV-2. We have employed a novel model which runs on a quantum-inspired device, and compared ours to a more traditional fingerprinting model, in search of compounds similar to RDV. Quantum or quantum-inspired computing allows for handling of complex information such as 3D structures which can increase accuracy, while having shorter execution times than those of regular computers. The two methods yielded different compounds, with some overlap. Our quantum-inspired model predicted BMS-986094 and the fingerprint model predicted different forms of cobalamin, also known as vitamin B12, as second-best candidates. We assessed the effect of different concentrations of BMS-986094 and vitamin B12 forms on SARS-CoV-2 infection in two different cell lines. BMS-986094 and vitamin B12 forms were effective at inhibiting replication of all variants of SARS-CoV-2 assessed, namely England 2 (England 02/2020/407073), B.1.1.7 (Alpha), B.1.351 (Beta) and 55 B.1.617.2 (Delta). Lastly, we demonstrated direct inhibition the viral RNA polymerase by all compounds in vitro. Our data demonstrate the effectiveness of our model, which performed similarly to the well-established Tanimoto fingerprinting in the case of RDV. QUBO may be employed in other disciplines, where local computational resources may not suffice, and quantum-inspired devices may be more appropriate when the modelling of complex structures may otherwise not be feasible.