Identification of compounds into the anatomical therapeutic chemical (ATC) system is crucial for drug development and basic research. It has been researched for over a decade since its initial proposal by Dunkel et al. in 2008. For a given compound, the task is to identify its ATC codes that are associated with its therapeutic, pharmacological, and chemical properties.
데이터셋
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The benchmark ATC-SMILES is built for ATC classification. ATC-SMILES consists of 4545 compounds/drugs and their SMILES sequences. The benchmark is with the maximum coverage (81.34%) of KEGG dataset which contains all 5588 known drugs/compounds used for ATC analysis. Prior to this benchmark, the most widely adopted one is Chen-2012 which covers 3883 (69.49%) drugs in KEGG and is mainly used for generating inter-drug correlations (e.g. STITCH). The two benchmarks are compared in Table 1. ATC-SMILES is designed to be inclusive to Chen-2012, but there are 2.16% misalignment due to the mismatching of drug IDs that we will explain soon. ATC-SMILES can be extended with new drugs much easier than previous benchmarks as long as the SMILES sequences are available. Trails/experiments are not a must.
ATC-GRAPH is the most extensive ATC benchmark dataset. All drugs in the benchmarks are linked to their Mol files instead of the SMILES sequences utilized in earlier benchmarks. This shift allows for more precise and detailed modeling and learning. In terms of scale, ATC-GRAPH surpasses Chen-2012 and ATC-SMILES by 36.78% and 16.85%, respectively. Significantly, ATC-GRAPH was curated through a cross-validation process involving multiple resources such as KEGG, PubChem, ChEMBL, ChemSpider, and ChemicalBook. This results in ATC-GRAPH being distinguished by its timeliness and comprehensive coverage across all five levels and drug genres.