Fig 1.
Overview of the LAMFP algorithm’s architecture.
Given an unseen drug, a lookup mechanism is used to identify chemically similar drugs which are used as input to AMFP. AMFP performs matrix factorization on the interaction graph adjacency matrix, followed by propagation of the drug’s representation to interacting drugs.
Fig 2.
Predicting the existence or absence of interaction between a new and an existing molecule using LAMFP.
Step1: a new molecule is lookup using the lookup mechanism, m = 3 similar drugs, and the drug’s similarity are retrieved. Step 2: the DDI between the m drugs and the existing drug is predict. Step 3: the result is the average score weighted by similarity.
Table 1.
DrugBank’s drug-drug interaction statistics (only new information is presented for version 5.1.6).
The two releases were used to perform a retrospective analysis.
Fig 3.
Comparative distribution of negative and positive drug-drug interactions across three test subsets.
This bar chart displays the distribution of negative (non-existing according to current knowledge) and positive (existing) drug-drug interactions across three distinct test subsets: the comprehensive subset (denoted as ‘All’), the subset with new-new drug pairings, and the subset with known-new drug pairings. It’s evident that negative interactions prevail in each subset, while positive interactions constitute a considerably smaller segment of the analyzed pairs.
Table 2.
The AUC, MRR and AUPR values for all methods and test subsets.
(Bold: Best score).
Table 3.
The accuracy, specificity, and sensitivity measurements for all methods and test subsets.
(Bold: Best score).
Fig 4.
MAP@k for all methods and test subsets.
Table 4.
The AUC, MRR and AUPR measurements for all similarity measures with LAMFP for all test subsets.
(Bold: Best score).
Table 5.
The accuracy, specificity, and sensitivity measurements for all similarity measures with LAMFP for all test subsets.
(Bold: Best score).
Fig 5.
Plot illustrating prediction error against molecular similarity, with color gradient denoting molecular weight.
Heavier molecules tend to show lower prediction accuracy.