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1. Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing

   https://doi.org/10.1038/s41467-021-21056-z  

   Belyaeva, Anastasiya; Cammarata, Louis; Radhakrishnan, Adityanarayanan; Squires, Chandler; Yang, Karren Dai; Shivashankar, G. V.; Uhler, Caroline (February 2021, Nature Communications)

   Abstract Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs. 

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2. CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability

   https://doi.org/10.1186/s13321-025-00976-8  

   Kyro, Gregory W; Martin, Matthew T; Watt, Eric D; Batista, Victor S (December 2025, Journal of Cheminformatics)

   Abstract The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. It is therefore of tremendous interest to develop advanced methods for identifying hERG-active compounds in the early stages of drug development, as well as for proposing redesigned compounds with reduced hERG liability and preserved primary pharmacology. In this work, we present CardioGenAI, a machine learning-based framework for re-engineering both developmental and commercially available drugs for reduced hERG activity while preserving their pharmacological activity. The framework incorporates novel state-of-the-art discriminative models for predicting hERG channel activity, as well as activity against the voltage-gated Na_V1.5 and Ca_V1.2 channels due to their potential implications in modulating the arrhythmogenic potential induced by hERG channel blockade. We applied the complete framework to pimozide, an FDA-approved antipsychotic agent that demonstrates high affinity to the hERG channel, and generated 100 refined candidates. Remarkably, among the candidates is fluspirilene, a compound which is of the same class of drugs as pimozide (diphenylmethanes) and therefore has similar pharmacological activity, yet exhibits over 700-fold weaker binding to hERG. Furthermore, we demonstrated the framework's ability to optimize hERG, Na_V1.5 and Ca_V1.2 profiles of multiple FDA-approved compounds while maintaining the physicochemical nature of the original drugs. We envision that this method can effectively be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug development programs that have stalled due to hERG-related safety concerns. Additionally, the discriminative models can also serve independently as effective components of virtual screening pipelines. We have made all of our software open-source athttps://github.com/gregory-kyro/CardioGenAIto facilitate integration of the CardioGenAI framework for molecular hypothesis generation into drug discovery workflows. Scientific contribution This work introduces CardioGenAI, an open-source machine learning-based framework designed to re-engineer drugs for reduced hERG liability while preserving their pharmacological activity. The complete CardioGenAI framework can be applied to developmental compounds exhibiting hERG liabilities to provide a means of rescuing drug discovery programs facing hERG-related challenges. In addition, the framework incorporates novel state-of-the-art discriminative models for predicting hERG, Na_V1.5 and Ca_V1.2 channel activity, which can function independently as effective components of virtual screening pipelines. 

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3. Pharmacological Effects of Cannabinoids Extracted from Industrial hemp on Epilepsy

   Patel, Aayushi; Agili-Shaban, Ruba; Patel, Shrina; Proano, Renata; Riarh, Fatima; Hasan, Eman; Potlakayala, Shobha; Rudrabhatla, Sairam (November 2022, . Journal of Pharmacy and Pharmacology Research)

   Epilepsy is a disease caused by abnormal brain activity due to disturbed nerve cell activity. It is the fourth most common neurological disorder. Only about 7 out of 10 individuals with epilepsy are successfully treated using anti-epileptic drugs. In the pharmaceutical industry, there has been a growing demand for cannabinoids from Cannabis sativa, commonly known as marijuana, for therapeutic, clinical, and nutraceutical supplements. More recently, the legalization of marijuana for clinical research has allowed to further explore the efficacy in the treatment of several neurological disorders like epilepsy. Unlike opioids, cannabinoids are not psychoactive, making it a potentially more favorable therapeutic drug. Most studies showing the efficacy of Cannabis as a treatment strategy point to the pain management associated with the binding of endocannabinoid G coupled protein receptors CB1 and CB2. Though there are cannabinoid therapeutic drugs like Epidiolex approved by the Food and Drug Administration (FDA), plant-based natural compounds are safer, and effective with no side effects. 

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4. Network medicine framework for identifying drug-repurposing opportunities for COVID-19

   https://doi.org/10.1073/pnas.2025581118  

   Morselli Gysi, Deisy; do Valle, Ítalo; Zitnik, Marinka; Ameli, Asher; Gan, Xiao; Varol, Onur; Ghiassian, Susan Dina; Patten, J. J.; Davey, Robert A.; Loscalzo, Joseph; et al (April 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development. 

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5. Mining heterogeneous network for drug repositioning using phenotypic information extracted from social media and pharmaceutical databases

   https://doi.org/j.artmed.2019.03.003  

   Yang, Christopher C.; Zhao, Mengnan (May 2019, Artificial intelligence in medicine)

   Drug repositioning has drawn significant attention for drug development in pharmaceutical research and industry, because of its advantages in cost and time compared with the de novo drug development. The availability of biomedical databases and online health-related information, as well as the high-performance computing, empowers the development of computational drug repositioning methods. In this work, we developed a systematic approach that identifies repositioning drugs based on heterogeneous network mining using both pharmaceutical databases (PharmGKB and SIDER) and online health community (MedHelp). By utilizing adverse drug reactions (ADRs) as the intermediate, we constructed a heterogeneous health network containing drugs, diseases, and ADRs, and developed path-based heterogeneous network mining approaches for drug repositioning. Additionally, we investigated on how the data sources affect the performance on drug repositioning. Experiment results showed that combining both PharmKGB and MedHelp identified 479 repositioning drugs, which are more than the repositioning drugs discovered by other alternatives. In addition, 31% of the 479 of the discovered repositioning drugs were supported by evidence from PubMed. 

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