The component dedicated to data retrieval and integration extracts information on drug-drug similarity relationships. As such, it builds a drug-drug similarity network based on indirect drug-disease relationships, using data from DrugBank Wishart et al. (2018) and DisGeNet Piñero et al. (2020). Indeed, neither database provides direct connections between drugs and diseases; therefore, we first combined DrugBank (containing data on drug-gene interactions) and DisGeNet (containing data on gene-disease relationships) to create a drug-gene-disease tripartite network. We used a Node. js script to import and process the data. Then we build the tripartite network as a graph G = V , E , where V represents the set of vertices or nodes, and E the set of edges or links between the nodes. In our tripartite network, set V is the union of the three disjoint sets of vertices represented by drugs/medicines ( V m ) , genes ( V g ) , and diseases ( V d ) , with V = V m ∪ V g ∪ V d . The set of edges E is the union of the disjoint sets of drug-gene edges ( E g ) and gene-disease edges ( E d ) , E = E g ∪ E d . The directed edges e i j g ∈ E g represent the interaction between v i m ∈ V m and v j g ∈ V g (i.e., drugs and genes), and the directed edges e i j d ∈ E d represent the interaction between v i g ∈ V g and v j d ∈ V d (i.e., genes and diseases).

Next, we projected the tripartite network into a monopartite drug-drug similarity network G s = V m , E s , where the vertices are medicines ( V m ) linked by similarity edges ( E s ) . A similarity undirected edge e i j s ∈ E s between v i m and v j m ∈ V m exists if there is at least one path between a drug/medicine v i m and a disease v k d ∈ V d and at least one path between v j m and the same disease v k d in G . The similarity network G s is weighted, with the weight of e i j s representing the number of w diseases v k d ∈ V d that are connected (via a path) to both v i m and v j m in the tripartite graph G . A connecting path between v i m ∈ V m and v j d ∈ V d exists in the tripartite graph G if we have a gene v k g ∈ V g such that ∃ two directed edges, e i k g ∈ E g from v i m to v k g ∈ V g and e k j d ∈ E d from v k g to v j d ∈ V d . According to the formal description above, the weight of a link/edge in the DDSN G s (representing the similarity relationship between two drugs/medicines) is the number of diseases that can be reached via valid connecting paths in the tripartite graph from both drugs.

Figure 2 presents an example of tripartite (drug-gene-disease) graph projection onto a drug-drug similarity network according to the algorithm described in this section; the example uses a small subgraph extracted from G .

Network community detection (also known as graph clustering) identifies groups of nodes/vertices that we call communities or clusters ( C 1 , C 2 , … C n , with C 1 ∪ C 2 ∪ … ∪ C n = V m ), which are more densely connected internally than the rest of the graph. We perform network community detection in Wolfram Mathematica 13 Inc. (2023) using the FindGraphCommunities function with the ‘Hierarchical’ method, based on vertex similarity and a dendrogram to represent nested community structures.

For each cluster C i ( i = 1 . n ̄ ) , we automatically assign a cluster’s dominant level 1 ATC code as its label and add drugs deviating from the cluster label to the drug repositioning hint list. We do this by computing cluster C i ’s ATC code histogram k i = k 1 i , k 2 i , … k W i (where W is the number of distinct ATC codes in C i and k j i the number of drugs in C i that have ATC code j ); as such, the dominant ATC code is j if max k 1 i , k 2 i , … k W i = k j i . As a result, we add all drugs v ∈ C i that do not have the dominant ATC level 1 code j (according to DrugBank) to the repositioning list as potentially having the property described by j .

DrugBank lists all associated level 4 ATC codes for each target; therefore, if we establish the dominant level 4 ATC codes in each cluster, we can identify the cluster’s targets of interest, which will subsequently foster the molecular docking validation efforts. Accordingly, for all drugs within the cluster labeled with the dominant level 1 ATC, we systematically compute the histograms corresponding to their levels 2, 3, and 4 of ATC and also identify the dominant level 4 ATC codes in each cluster, map them with their associated biological targets from DrugBank, and associate these targets with drug repositioning hints. Specifically, if the dominant ATC level 4 code in C i corresponds to the mechanism of action a , then we retrieve from DrugBank the list of targets associated with that mechanism of action T a .

Given that we can assemble drugs according to a specific mechanism of action (described by a level 4 ATC code) and its corresponding biological targets, our approach uses shared pharmacological properties to suggest new therapeutic uses. For example, a drug not known to have the community-dominant ATC level 4 code could be uncovered as binding to a target shared by the dominant group; this might be effective for a disease treated by the community’s dominant drug class.

The targets we identify to associate with the drug repositioning hints facilitate molecular docking (by calculating their binding affinity to the assigned targets), thus prioritizing drugs with favorable interaction profiles. This strategy improves the efficiency of subsequent experimental validation by focusing on biologically plausible hypotheses.

We validate repositioning candidates by automatically querying the PubMed database with Biopython Cock et al. (2009). Explicitly, we assign the Medical Subject Headings (MeSH) terms corresponding to each drug name in the repositioning candidate list, its community level 1 ATC name, and relevant synonyms. We used the Boolean operator ‘AND’ to correlate the drug name and the ATC category name, and ‘OR’ to include relevant synonyms of the ATC category. To rely only on high-quality evidence, we applied filters that retrieve only research articles and clinical trials, excluding commentaries and unrelated studies. Subsequently, we pass the retrieved literature list through expert analysis that filters the publications and further confirms the predicted property.

Validation of drug repositioning hints with the latest literature and electronic health databases is paramount. The main problem in drug repositioning is that we do not have a robust ground truth: we can rely on what we know about drugs (i.e., current knowledge), but we cannot rely on negative information. In other words, if we do not know, for instance, that there is a specific drug-target interaction, it does not necessarily mean that the interaction does not exist; maybe it exists, but we do not know about it yet Udrescu et al. (2023). Therefore, most available drug repositioning pipelines based on machine learning methods adopt the train/test dataset split strategy to assess their performance. However, such a strategy cannot work well in the case of network-based approaches, as it entails affecting the network topology; this is why network-based methods rely on external validation for performance assessment. External validation can be performed by automatic/systematic literature or electronic health databases, molecular docking, in vitro, or in vivo actual experiments. Unfortunately, in vitro and in vivo experiments require extensive resources and demand carefully designed protocols, meaning serious additional research. On the other hand, molecular docking requires substantial computational resources, including months of program execution run time. Therefore, validation of drug repositioning hints with the latest literature and electronic health databases remains the most affordable and feasible solution (adopted by most state-of-the-arthe weight of the similarity relationship between two drugs/medicines is the number of diseases that can be reached via valid paths in the tripartite graph from both drugs t pipelines in Table 2).

Figure 3, shows that our DDSN network-building and clustering methodology produces 34 clusters. However, we excluded from further analysis all clusters disconnected from the main connected component (clusters 15–21 and 23–31) and clusters with fewer than eight vertices/drugs (clusters 13, 14, 22, 32–34). Consequently, we investigate the remaining 12 clusters in the drug-drug similarity network (Figure 3) as follows:

1. Scan the level 1 ATC of all drugs in DrugBank and automatically label the cluster with its predominant level 1 ATC property (see the first four columns in Table 2).

Our results show that 53.4% of drugs have the ATC level 1 property given by their cluster label, and 20.2% are not formally classified in the ATC level 1 cluster label with DrugBank data Wishart et al. (2018), but the automated literature check demonstrates the predicted corresponding anatomical/pharmacological properties (see our GitHub main results). Consequently, 26.4% of the drugs do not comply with the cluster label, so we consider them candidates for repositioning on the property corresponding to their community/cluster label (see Table 2). In addition, this means that the accuracy of our pipeline, measured with the available information (that is, current knowledge), is 53.4 % + 20.2 % = 73.6 % .

ATC level 1 codes describe the main anatomical or pharmacological drug groups. ATC level 4 codes correspond to the chemical, pharmacological, or therapeutic drug subgroups. Our method identifies the dominant ATC level 1 and level 4 codes of the cluster and proposes them as new anatomical and physiological target groups and potential mechanisms of action for repositioning candidates. Figure 4 illustrates the labeling process for Cluster 1, where the dominant level 1 ATC code is N (Nervous System); therefore, N is automatically assigned as the cluster’s label, as shown in panel (a). Panel (b) presents a histogram of drugs distributed across their level 2 ATC codes, with N05–Psycholeptics as the dominant category. Panel (c) further refines this distribution at level 3 ATC, showing that the majority of drugs fall under N05C–Hypnotics and Sedatives and N03–Antiepileptics. Analysis of level 4 ATC codes reveals the top three categories, N05CB and N05CA (barbiturates), and N03AX (Other Antiepileptics), depicted in panel (d). We extract all nervous system-related targets associated with N05CB, N05CA, and N03AX drugs from DrugBank and propose their evaluation via molecular docking to identify potential candidates for repositioning within Cluster 1.

Our pipeline generates a list of drug candidates for repositioning at varying first ATC levels. The project README in our GitHub (https://github.com/GrozaVlad/Drug-repurposing-using-DDSN-with-disgenet/blob/main/results/README.md) presents two result files: Literature validation. xlsx, which provides the PubMed link(s) and year of publication for literature supporting the predicted properties, and Repositioning hints and predicted targets. xlsx, which lists the pipeline’s the top 3 ATC level 4 codes (ranked by frequency within the community) and the DrugBank targets associated with drugs in those ATC level 4 groups.

Given the challenge of finding anticancer therapies, we selected an old drug from an L-labeled community to further validate our method in silico with molecular docking. To this end, we selected chloramphenicol, which has been in clinical practice for decades; its pharmacokinetics, safety profile, and side effects are well understood, which can expedite its repositioning as a cancer therapeutic. In addition, chloramphenicol is inexpensive, making it an attractive option for cancer treatment from economic considerations. The existing approval for chloramphenicol as an antibacterial agent and its age could simplify the regulatory pathway for repositioning in cancer and may lead to faster clinical trials and approval.

Chloramphenicol belongs to Cluster 6, where 60.8% of drugs have ATC level 1 code L–Antineoplastic and immunomodulating agents (see Figure 5A), so we add chloramphenicol to the drug repositioning hint list as an anticancer drug. Inspection of the higher ATC levels of L drugs reveals their distribution across ATC levels 2 as follows: 23 drugs have code L01–Antineoplastic agents (e.g., pentostatin, dasatinib, axicabtagene ciloleucel), eight drugs have L04–Immunosuppressants (e.g., cladribine, tofacitinib, abatacept), and one has L03–Immunostimulants (namely, aldesleukin) (Figure 5B). Next, the ATC level 3 distribution analysis shows that sublevel L01E–Protein kinase inhibitors dominates with 11 drugs, followed by L04A–Immunosuppressants with eight drugs (Figure 5C). Figure 5D illustrates the distribution of drugs on ATC level 4: eight drugs have code L04AA–Selective immunosuppressants, five drugs each have L01FX–Other monoclonal antibodies and antibody drug conjugates and L01XX–Other antineoplastic agents, respectively, and 3 drugs each have L01EM–Phosphatidylinositol-3-kinase (PI3K) inhibitors and L01EL–Bruton’s tyrosine kinase (BTK) inhibitors, respectively.

According to our repositioning pipeline, we assess the top 3 level 4 ATC codes in Cluster 6 to find the repositioning candidate targets. The seven selective immunosuppressor drugs labeled as L04AA target various proteins, such as the ribonucleoside-diphosphate reductase protein group (e.g., RRM1, RRM2, and RRM2B) and the catalytic subunits of the DNA polymerase (e.g., POLA1, POLE, POLE2, POLE3, and POLE4). Monoclonal antibody drugs within the L01FX subgroup target Fc-gamma I, IIa, III-A, III-B, cytotoxic T-lymphocyte protein 4, and many other specific biological targets. The subgroups L01XX, L01EM, and L01EL are in third place. The L01XX subgroup includes other antineoplastic agents for which DrugBank lists targets, such as adenosine deaminase, B-lymphocyte antigen CD19, interleukin-2 receptor subunits alpha and beta, G1/S-specific cyclin-D1, and transcription factor Jun. L01EM drugs are phosphatidylinositol 3-kinase (PI3Ks) inhibitors, and L01EL are Bruton’s tyrosine kinase (BTK) inhibitors.

We employ molecular docking simulations as a computational screening tool to validate the drug repositioning candidates identified from network-based clustering and ATC code labeling. This approach tests the hypotheses generated by our pipeline, which assigns biological targets to drugs with divergent level 4 ATC codes within each cluster, as described in Section 3.2.

We performed molecular docking on the crystallographic structures of Bruton’s tyrosine kinase (BTK1) and the alpha, gamma, and delta isoforms of phosphoinositide 3-kinase (PI3K), all of which belong to Homo sapiens. We obtained the crystallographic structures of the target proteins from the Protein Data Bank: PDB codes 5P9I for BTK1 Bender et al. (2017) and 7K6M, 8SC8, and 5M6U for PI3K alpha, gamma, and delta isoforms, respectively (see Figures 6A–D) Cheng et al. (2020); Erra et al. (2017). In 5P9I, BTK1 is co-crystallized with the known inhibitor ibrutinib; the PI3K isoforms in 7K6M, 8SC8, and 5M6U are co-crystallized with synthetic ligands VXY, D0D, and 7KA, respectively (see Figure 7).

We adopted a protein-based approach to study the mode of interactions with the enzyme active site, using a protocolalready adopted in our previous studies Tundis et al. (2023); Perri et al. (2023). As a first step of our in silico experiments, a re-docking calculation was performed to determine the binding energy values of the crystallographic ligands for each target protein (see Figure 6E); we used these values as a reference for subsequent simulations.

The molecular structures of alpelisib, copanlisib, idelalisib, duvelisib, and chloramphenicol were built using Avogadro modeling software Hanwell et al. (2012). We employed AutoDock Vina 1.1.2 for docking calculations Trott and Olson (2010). Preliminary conversion of the structures from the PDB format was performed using the AutoDock Tools 1.5.6 graphical user interface Morris et al. (1998). During the conversion, we added polar hydrogens to the crystallographic enzyme structures and merged the ligands’ apolar hydrogens with the carbon atoms to which they are attached. Full flexibility was ensured for the ligands, resulting in four active torsions for duvelisib, five for alpelisib, idelalisib, chloramphenicol, and nine for copanlisib. We conducted all simulations for each compound to a very high degree of exhaustiveness. We analyzed the ligand binding modes through visual inspection and evaluated the intermolecular interactions using the automated protein-ligand interaction profiler, PLIP Salentin et al. (2015).

In the modern approach to scientific research, computational techniques provide significant advantages for streamlining drug discovery or uncovering the biological properties of natural or synthetic compounds Grande et al. (2020), (2021). Similarly, these techniques can support the discovery of additional pharmacological activities of known drugs used to treat diseases other than those for which they are currently approved Fadlalla et al. (2022).

The possibility of reusing drugs with already established safety profiles and pharmacokinetics offers the extra advantage of significantly lowering the costs and time required for the standard drug discovery process. To this end, encouraged by the results of previous studies Grande et al. (2020), we conducted molecular docking studies to explore the potential of chloramphenicol to directly interact with Bruton’s tyrosine kinase 1 (BTK1) and the alpha, gamma, and delta isoforms of phosphoinositide 3-kinase (PI3K).

BTK1 is a kinase protein containing five domains: an amino-terminal pleckstrin homology (PH) domain, a proline-rich TEC homology (TH) domain, SRC homology (SH) domains SH2 and SH3, and a protein kinase domain endowed with tyrosine phosphorylation activity Pal Singh et al. (2018).

As a result, even though the chloramphenicol–5P9I estimated binding energy ( − 7.5 kcal/mol) is lower than that obtained for the crystallographic ligand, it is still a value compatible with a stable complex. Furthermore, chloramphenicol accommodates into the protein binding site, occupying the same position as ibrutinib (Figure 8). The ligand-target complex is stabilized by a hydrogen bond with Lys 430 and hydrophobic and van der Waals interactions with other key residues of the active site (Table 3).

Class I PI3K includes two subclasses: IA (PI3K alpha, beta, delta) and IB (PI3K gamma). All of them function as heterodimers, consisting of a catalytic subunit (p110) and a regulatory subunit (p85 for subclass IA and p84/p87 for subclass IB, respectively) Vanhaesebroeck et al. (2010). Previous studies using multiple sequence alignment of protein sequences from available crystallographic structures of PI3K identified key residues in the binding regions of each isoform. For PI3K alpha, the critical residues include Ser 774, Trp 780, Asp 810, Tyr 836, Val 851, and Asp 933. Other residues, such as Ser 773, Asn 853, Ser 854, His 855, and Gln 859, appear important for ligand binding. For PI3K gamma, the key residues include Val 882, Asp 964, Tyr 867, Ser 806, and Lys 833. In the case of PI3K delta, Val 828, Trp 760, Lys 779, Glu 826, and Tyr 813 are significant for ligand binding. Identifying compounds interacting with these residues may help develop selective inhibitors for each PI3K isoform Al Hasan et al. (2023).

Considering these details and our preliminary results, we performed molecular docking experiments on the alpha, gamma, and delta isoforms of PI3K to compare the interaction mode of known ligands with that of chloramphenicol. Specifically, to investigate the interaction of chloramphenicol with the PI3K alpha isoform, we used the structure retrieved from PDB with code 7K6M, which corresponds to the enzyme’s catalytic subunit. In this structure, the protein is co-crystallized with a selective morpholine inhibitor, (S)-2,2-difluoroethyl-3-((2′-amino-5-fluoro-2-morpholino-[4,5′-bipyrimidin]-6-yl)amino)-3-(hydroxymethyl)pyrrolidine-1-carboxylate, referred to as VXY, discovered through structure-based drug design (SBDD) and computational analysis Cheng et al. (2020).

For a more comprehensive understanding of chloramphenicol’s behavior in its interaction with the target protein, we aimed to compare its binding mode with those of known ligands, such as alpelisib and copanlisib. Accordingly, we docked all compounds with 7K6M. As a result, chloramphenicol shares a similar orientation within the active site as the crystallographic ligands, alpelisib and copanlisib (Figure 9), interacting with Ser 774 throughout a hydrogen bond and with other key binding site residues through hydrophobic and van der Waals interactions (Table 4). The calculated binding energy for the chloramphenicol–7K6M complex, although less favorable than those observed for known ligands, seems to support our hypothesis of a direct interaction between chloramphenicol and PI3K alpha.

We performed similar docking experiments on the crystallographic structure of PI3K gamma (PDB code 8SC8) to assess the binding mode of the studied ligand to this target. In this case, we compared the interaction of chloramphenicol to that of the crystallographic ligand D0D—(R)-N-(2-chloro-5-(4-((1-phenylethyl)amino)quinazolin-6-yl)pyridin-3-yl)methanesulfonamide—and the known inhibitors idelalisib and duvelisib.

Our simulation experiments demonstrated that all the ligands occupied the same region as the crystallographic ligand, corresponding to the enzyme’s catalytic subunit (Figure 10). Chloramphenicol interacted with key residues of the protein active site, although its binding energy values were less favorable than those observed for idelalisib and duvelisib (Table 5).

To further assess the reliability of our experiments, we also tested the interaction between chloramphenicol and the PI3K delta isoform. We docked chloramphenicol with the kinase catalytic subunit of the protein (PDB code 5M6U). Its binding mode was compared to that of the crystallographic ligand 7 KA—(S)-2-(1-((6-amino-5-ethynylpyrimidin-4-yl)amino)ethyl)-3-phenylpyrrolo[2,1-f][1,2,4]triazin-4(3H)-one. For a better perspective, we also docked the known inhibitors idelalisib, duvelisib and copanlisib with the selected protein structure.

Chloramphenicol shared a similar orientation within the active site as the known ligands and interacted with key residues for the catalytic activity (Figure 11). Furthermore, the binding energy value for the chloramphenicol-5M6U complex was comparable to that calculated for the crystallographic ligand, although less favorable than those observed for the known inhibitors (Table 6).

Our automated literature analysis revealed that 20.2% drugs exhibit pharmacological properties aligned with their assigned ATC level 1 category, although their ATC labels according to DrugBank do not match their community labels. For all such cases, the literature provides experimental or clinical evidence supporting the drugs’ mechanisms of action, therapeutic applications, or pharmacological effects that correspond to their respective ATC code labeling.

Here, we provide several examples of drugs for which our repositioning method recovers pharmacological properties confirmed by the literature beyond those assigned by ATC codes. Sildenafil is a versatile molecule with famous repositioning stories, from vasodilator and platelet aggregation inhibitor to penile erection and later vasodilator in pulmonary arterial hypertension Jourdan et al. (2020). DrugBank lists sildenafil in the G–Genito urinary system and sex hormones category as a urological drug used to treat erectile dysfunction. In our drug-drug similarity network, sildenafil belongs to Cluster 1, labeled as N–Nervous system. Indeed, Xiong and Wintermark review the clinical evidence for the effects of sildenafil on the extent of brain injury, myelination neuroinflammation, and brain function in adults and neonates; they also indicate the clinical trials that test the effects of sildenafil seen in animal models in human newborns and after birth asphyxia Xiong and Wintermark (2022). In addition, another review presents in vitro and mouse studies, systematic review, and pilot patient studies reporting the effectiveness of sildenafil in Alzheimer’s disease Sanders (2020).

Spironolactone is another example of how the literature confirms the repositionings recovered by our methodology. Spironolactone is an anti-aldosterone diuretic, officially included in the category of C–Cardiovascular system drugs (i.e., C is its first ATC level). Our repositioning method places spironolactone within Cluster 5, which has the ATC level 1 label D–Dermatologicals. The review articles by Aguilar Medina et al. (2022); Searle et al. (2020) confirm the beneficial effects of spironolactone in androgen-mediated skin conditions, such as hidradenitis suppurativa, acne, alopecia pattern in women and hirsutism; they also highlight that spironolactone is well tolerated and has a favorable safety profile, i.e., it has few adverse effects, at doses ranging from 25 to 200 mg/day.

One more example of recovered repositionings, amitriptyline, traditionally classified as an antidepressant, is found by our method in cluster L—Antineoplastic and immunomodulating agents. In fact, amitriptyline has potential in cancer treatment through various mechanisms. For example, in multiple myeloma (MM) xenograft models, amitriptyline decreases tumor growth and prolongs survival by inducing p53, activating caspase-3, and reducing the anti-apoptotic proteins Bcl-2 and Mcl-1 Zhang et al. (2013). In colorectal cancer cells, amitriptyline and other tricyclic antidepressants reduce cell viability in a time-dependent manner Arimochi and Morita (2006). Furthermore, it inhibits cyclin D2 transactivation, arrests the cell cycle in G0/G1, and modulates histone acetylation by downregulating HDACs, particularly HDAC7, thus enhancing tumor suppressor gene expression Mao et al. (2011). Amitriptyline promotes TRAIL-mediated apoptosis by enhancing the expression of death receptors and caspase activation; it also suppresses autophagy, disrupts lysosomal-autophagosome fusion, and reduces oxidative stress markers, underscoring its antitumor properties Zheng et al. (2023).

Recovered repositioning examples, Table 2 (literature validation, showing the number of drugs per community with literature-confirmed ATC changes), and the exhaustive per-community target lists in the Repositioning hints and predicted targets table in our GitHub results allow readers to explore alternative candidates from other communities.

A promising result of our approach is the identification of chloramphenicol in Cluster 6. Chloramphenicol is a widely used antibacterial agent, but because it is in cluster 6, it represents a candidate for cancer repositioning. We perform molecular docking simulations to further analyze our computational predictions. Several previous studies have reported anticancer effects of chloramphenicol or its derivatives, including cytotoxicity in mesothelioma cells Giannopoulou et al. (2019), growth inhibition of T-leukemic cells by polyamine-conjugated derivatives Kostopoulou et al. (2015), and apoptosis induction in multiple myeloma cells through mitochondrial protein synthesis inhibition Tian et al. (2016). Furthermore, mitochondria-targeting antibiotics such as chloramphenicol have been shown to eradicate cancer stem cells across tumor types Lamb et al. (2015). However, no experimental binding studies have so far demonstrated a direct interaction of chloramphenicol with BTK1 or PI3K (alpha, gamma, and delta isoforms). Our docking simulations therefore provide the first in silico evidence that the drug may interact with these targets, suggesting a dual mechanistic hypothesis combining mitochondrial effects with kinases modulation, which is consistent with its reported anticancer activity and warrants future biochemical validation.

Our docking results indicate that chloramphenicol binds within the active site of BTK1 similarly to ibrutinib, a BTK1 inhibitor. Although chloramphenicol has a lower binding energy than ibrutinib, its complex is stabilized by key hydrogen bonds and hydrophobic interactions (Figure 8; Table 3), suggesting potential kinase inhibitory activity. Similarly, molecular docking of chloramphenicol with PI3K isoforms revealed that the drug binds to the target protein in a manner comparable to that of previously identified inhibitors, such as alpelisib, copanlisib, idelalisib, and duvelisib (Figures 9–11). Although its binding energies are less favorable than those calculated for known inhibitors, chloramphenicol interacts with key amino acid residues through hydrogen bonds and van der Waals forces (Tables 4, 5, 6), supporting its potential role in PI3K modulation. Future studies are required to determine whether chloramphenicol can be repurposed for cancer treatment, offering a cost-effective and adaptable therapeutic option.

Because the drug-drug similarity network projection treats a drug-gene edge as evidence of a pharmacological relationship without encoding the precise action (agonist, antagonist, substrate, etc.), a predicted repositioning can reflect either a potentially beneficial effect or an adverse/off-target effect on the same physiological system.

As an illustrative edge case, clarithromycin clusters in the Cluster 3–Cardiovascular system. Clarithromycin is clinically associated with QT-interval prolongation and increased arrhythmia risk—a well-known cardiovascular liability that counsels caution. Our network analysis nevertheless assigned clarithromycin in Cluster 3 because it is pharmacologically linked (via targets and shared-disease connectivity) to targets involved in cardiovascular regulation. One specific hypothesis emerging from the community mapping is a putative interaction with NR3C2 (the mineralocorticoid receptor). Antagonism of NR3C2 underlies the cardioprotective effects of established mineralocorticoid receptor antagonists (MRAs), which reduce heart-failure hospitalisation and cardiovascular death; MRAs are, however, associated with an increased risk of hyperkalaemia Jhund et al. (2024). While there is no conclusive evidence that clarithromycin antagonises NR3C2 in vivo, a retrospective clinical observation that clarithromycin co-administered with MRAs is associated with higher serum potassium levels provides indirect, functional context for an interaction with the mineralocorticoid system Hirai et al. (2023). Taken together, these lines of evidence make NR3C2 a biologically plausible target for further mechanistic investigation. However, any therapeutic hypothesis should be experimentally validated and carefully evaluated against clarithromycin’s known cardiac risks.

This study highlights the potential of our DDSN-based drug repositioning pipeline to identify new targets for existing drugs and facilitate molecular docking investigations. The pipeline accurately aligns 73.6% of the drugs with their cluster’s dominant level 1 ATC property and classifies the non-conforming remainder as repositioning candidates; overall, this means a good drug property prediction accuracy given the extent of unknown information. Furthermore, our automated pipeline also identifies biological targets that correspond to the majority of drugs within a cluster and proposes them as potential new targets for the repositioning of candidates.

In future studies, we can extend our definition of drug-drug similarity to simplicial complexes (which generalize the notion of graph by allowing higher-dimensional relationships between nodes, not just pairwise edges).

Future work may also validate the repositioning candidates identified by our method (see our GitHub results), by applying a combination of dry-lab approaches (i.e., molecular docking simulations) and wet-lab experiments (i.e., in vitro and in vivo tests) to confirm their new pharmacological properties and therapeutic potential.