Fig 1.
Flowchart of the MetaSynMT framework.
A. Drug feature construction: Drug representations are learned by aggregating information in the parasitic drug-target graph guided by meta-paths. B. Parasitic disease feature construction: The parasite disease–gene association matrix and the disease similarity matrix are independently processed by Multilayer Perceptron (MLP) to generate two feature matrices, which are subsequently concatenated. C. Prediction module: The drug–drug features and drug–drug–disease triplet features are input into the task-specific (side effect and synergy) prediction modules to produce corresponding prediction scores.
Table 1.
Summary of data types and numbers.
Fig 2.
Schematic illustration of drug representation learning.
(a) An example of parasitic drug-target graph. (b) types of meta-paths. (c) Representative instance sequences corresponding to meta-paths M2 and M3. (d) An example of
and
based on (c). (e) The process of hierarchical information aggregation from neighboring nodes at each layer to obtain the node embedding based on the specific meta-path Mk. (f) Aggregate information from all different types of meta-paths to obtain the final feature representation of drug node di.
Table 2.
Summary of data types and numbers in the parasitic drug-target graph.
Table 3.
Performance comparison of MetaSynMT and baseline methods on the parasitic disease benchmark dataset.
Table 4.
Performance comparison of MetaSynMT and baseline methods on the on-screen dataset.
Table 5.
Performance comparison of four different initial feature extraction methods on the parasite benchmark dataset.
Table 6.
Performance comparison of MetaSynMT and its variants for drug combination prediction.
Table 7.
Performance comparison of MetaSynMT and its variants with different meta-path aggregation strategies.
Fig 3.
(A) Performance metrics (AUC, AUPR, and ACC) under varying values of parameter K.
(B) Performance metrics (AUC, AUPR, and ACC) under varying embedding dimensions d. (C) Performance metrics (AUC, AUPR, and ACC) under different learning rates lr.
Fig 4.
Heat map of attention scores for four types of meta-paths.
Fig 5.
Meta-path visualization of the albendazole and tetrandrine combination.
Table 8.
The eight candidate synergistic and safe drug combinations targeting parasitic diseases.
Table 9.
The synergistic and safe drug combinations targeting echinococcosis.
Fig 6.
Survival curves of protoscoleces over 7 days with individual drugs at maximum concentrations.
Fig 7.
The dose-response matrix of the allicin and sodium stibogluconate combination.
Fig 8.
Compared with the negative control, DMSO solvent (1%), albendazole (15 μM), allicin (36.3 μM) or sodium stibogluconat (850 μM) alone, the combination of allicin (36.3 μM) and sodium stibogluconat (850 μM) has a significantly stronger inhibitory effect on echinococcosis protoscoleces.
Fig 9.
Heatmap and 3D surface plot of four synergy scores (ZIP, Bliss, HSA, and Loewe) for allicin and sodium stibogluconate combination.
Synergistic: synergy score > 10; Additive: –10 < synergy score < 10; Antagonistic: synergy score <–10. A t-test was used to calculate the P-values of the four synergy scores (p < 0.09), suggesting a trend toward significance.