All data analysis was performed with Python 3.10, with RDKit 2022.03.2 as cheminformatic toolkit. Compound structures for metabolites were the same as in our previous work, that is, the subset of "detected and quantified" and "detected but not quantified" molecules from the gut compartment in the Human Metabolome Database (HMDB) plus some additions obtained from literature searches, resulting in a total of 6663 molecules. Metabolites were retrieved irrespective of their origin: host, microbial, both, xenobiotics, and unknown, since in this work we are interested in the bidirectional communication between the host and the microbiome, that could also involve human metabolites, with bacterial proteins. Some comparative analyses used a subset of the DrugBank 45 (1410 molecules) that included the small molecules in approved, not-withdrawn, non-illicit status, and that were administered orally and acted systemically. A few shared compounds were manually assigned to the metabolite or drug set. Molecular structures were subject to the same pipeline as that described before for standardization. Biological interactions of these molecules with human or bacterial proteins were retrieved from both ChEMBL (release 33) and BindingDB, and were complemented with literature searches. Only data from well-defined protein or protein complexes, and obtained through dose-response analysis (standardized in the form of pCHEMBL or pCHEMBL-like values, that is -log10 (molar IC50, XC50, EC50, AC50, Ki, Kd or Potency)), were retrieved. In the case of bacterial proteins, only those of a set of 332 gut genera For the predicted interactions we used the SEA (Similarity Ensemble Approach) method, available in . Only interactions with p-value <= 1e-16 were retained, as they are considered by the program to be the most reliable ones.