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 CIDSID 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.
Ivanova et al. (Thu,) studied this question.