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Predicting Novel Functional Roles of Designed Small Biomolecules: An ML Approach Utilizing PubChem Compound and Substance Identifiers (CID-SID ML model)

Significance and Object: The proposed methodology aims to provide time- and cost-effective approach for the early stage in drug discovery. The machine learning models developed in this study used only the identification numbers provided by PubChem. Thus, a drug development researcher who has obtained a PubChem CID and SID can easily identify new functionality of their compound. The approach was demonstrated, using four bioassay which were on (i) the antagonists of human D3 dopamine receptors; (ii) the promoter Rab9 activators; (iii) small molecule inhibitors of CHOP to regulate the unfolded protein response to ER stress; (iv) antagonists of the human M1 muscarinic receptor. Solution: The four bioassays used for demonstration of the approach were provided by PubChem. For each bioassay, the generated by PubChem CIDs, SIDs were extracted together with the corresponding activity. The resulting dataset was sifted with the dataset on a water solubility bioassay, remaining only the compounds common for both bioassays. In this way, the inactive compounds were reduced. Then, all active compounds were added, and the resulted dataset was later used for machine learning based on scikit learn algorithms. Results: The average values of the ML models` metrics for the four bioassays were: 83.82% Accuracy with 5.35 standard deviation; 87.9% Precision with 5.04 standard deviation; 77.1% Recall with 7.65 standard deviation; 82.1% F1 with 6.44 standard deviation; 83.4% ROC with 5.09 standard deviation. Since the methodology was publicly available as a preprint, four more machine ML models have been developed. Their results are discussed in the "Results and Discussion" section.

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