The optimization of running time for a maximum common substructure-based algorithm and its application in drug design

• We optimize the running time of the MCS-based algorithm.
• 15–30 s is a reasonable range for selecting a candidate time threshold.
• We evaluate the statistical relationship between an MCS coefficient and functional similarity.
• The potential biological activity of small molecules with unknown functions can be predicted using our methods.

In the field of drug discovery, it is particularly important to discover bioactive compounds through high-throughput virtual screening. The maximum common substructure-based (MCS) algorithm is a promising method for the virtual screening of drug candidates. However, in practical applications, there is always a trade-off between efficiency and accuracy. In this paper, we optimized this method by running time evaluation using essential drugs defined by WHO and FDA-approved small-molecule drugs. The amount of running time allocated to the MCS-based virtual screening was varied, and statistical analysis was conducted to study the impact of computation running time on the screening results. It was determined that the running time efficiency can be improved without compromising accuracy by setting proper running time thresholds. In addition, the similarity of compound structures and its relevance to biological activity are analyzed quantitatively, which highlight the applicability of the MCS-based methods in predicting functions of small molecules. 15–30 s was established as a reasonable range for selecting a candidate running time threshold. The effect of CPU speed is considered and the conclusion is generalized. The potential biological activity of small molecules with unknown functions can be predicted by the MCS-based methods.

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