Data mining of arsenic-based small molecules geometrics present in Cambridge structural database.

Arsenic, ubiquitous in various industrial processes and consumer products, presents both essential functions and considerable toxicity risks, driving extensive research into safer applications. Our investigation, drawing from 7182 arsenic-containing molecules in the Cambridge Structural Database (CSD), outlines their diverse bonding patterns. Notably, 51% of these molecules exhibit cyclic connections, while 49% display acyclic ones. Arsenic forms eight distinct bonding types with other elements, with significant interactions observed, particularly with phenyl rings, O and F moieties. Top interactions involve carbon, nitrogen, oxygen, fluorine, sulfur, and arsenic itself. We meticulously evaluated average bond lengths under three conditions: without an R-factor cut-off, with R-factor ≤0.075, and with R-factor ≤0.05, supporting the credibility of our results. Comparative analysis with existing literature data enriches our understanding of arsenic's bonding behaviour. Our findings illuminate the structural attributes, molecular coordination, geometry, and bond lengths of arsenic with 68 diverse atoms, enriching our comprehension of arsenic chemistry. These revelations not only offer a pathway for crafting innovative and safer arsenic-based compounds but also foster the evolution of arsenic detoxification mechanisms, tackling pivotal health and environmental challenges linked to arsenic exposure across different contexts.