IUPAC-induced computational approaches for identifying boosters of small biomolecule functionality: A case study of human tyrosyl-DNA phosphodiesterase 1 (TDP1) inhibitors

This paper introduces several proof-of-concept (PoC) computational methods intended to offer biochemical researchers straightforward, time- and cost-effective strategies to accelerate their work. While Machine Learning (ML) models were developed, the study's central purpose was to explore approaches for the identification of desirable functional groups/fragments in small biomolecules regarding a specific functionality, which, in this case, was human tyrosyl-DNA phosphodiesterase 1 (TDP1) inhibition. This was achieved primarily by tokenising IUPAC names to generate features. Additionally, the applicability of the CID_SID ML model for predicting TDP1 activity was developed and explored. Since these computational approaches were not experimentally validated due to a lack of appropriate laboratory facilities, they are presented as open proposals for further laboratory investigation. • New IUPAC Framework: Explicitly tokenizes names to extract structural fragments for human-centric intelligent insights. • Interpretability: Used IUPAC tokens and LIME for structural insights, bridging statistical models and medicinal chemistry. • Statistical Enrichment: Ranked "activity-boosting" TDP1 inhibitor groups via Fisher's exact test for chemical insights. • Benchmarking: Validated nomenclature-based models against MORGAN2 and RDKit/SMILES benchmarks using large bioassay datasets. • Risk-Informed Support: A rapid screening tool to identify high-risk drug candidates early, reducing clinical failure rates.