1st International Conference on Chemo and BioInformatics, ICCBIKG 2021, (446-449)
AUTHOR(S) / АУТОР(И): Izudin Redžepović, Boris Furtula
E-ADRESS / Е-АДРЕСА: izudin.redzepovic@pmf.kg.ac.rs, furtula@uni.kg.ac.rs
DOI: 10.46793/ICCBI21.446R
ABSTRACT / САЖЕТАК:
The idea of quantifying similarity between compounds may be traced back to the roots of contemporary chemoinformatics. At present, there is a number of coefficients that are used as similarity metrics. Many of them are defined as to measure coherence among two structural fingerprints, and usually yield similarity results between 0 and 1. However, there are indices that capture dissimilarity between molecular structures. This paper reports results on a comparative investigation of the several similarity coefficients on a set of compounds with the physiological responses. These molecules induce diverse body sensations that range from pleasant feelings up to euphoria and analgesia. Some of them are well-known drugs. In order to quantify molecular structure, Morgan circular fingerprints have been applied, which are frequently used in similarity calculations. This statistical analysis reveals which indices tend to produce higher structural similarity results and opposite.
KEY WORDS / КЉУЧНЕ РЕЧИ:
molecular structure, drugs, similarity coefficients, Morgan fingerprints, statistical analysis.
REFERENCES / ЛИТЕРАТУРА:
- a. Bender, R.C. Glen., Molecular similarity: a key technique in molecular informatics, Organic & Biomolecular Chemistry, 2 (2004) 3204-3218.
- A. G. Maldonado, J.P. Doucet, M. Petitjean, B.T. Fan., Molecular similarity and diversity in chemoinformatics: from theory to applications, Molecular Diversity, 10 (2006) 39-79.
- C. W. Coley, L. Rogers, W.H. Green, K.F. Jensen., Computer-assisted retrosynthesis based on molecular similarity, ACS Central Science, 3 (2017) 1237-1245.
- Y. Liu, Y. Cao, W. Lai, T. Yu, Y. Ma, Z. Ge., A strategy for predicting the crystal structure of energetic N-oxides based on molecular similarity and electrostatic matching, CrystEngComm, 23 (2021) 714-723.
- M. D. Krasowski, A.F. Pizon, M.G. Siam, S. Giannoutsos, M. Iyer, S. Ekins., Using molecular similarity to highlight the challenges of routine immunoassay-based drug of abuse/toxicology screening in emergency medicine, BMC Emergency Medicine, 9 (2009) 1-18.
- L. Martin, T.F. Willems, L.C. Lin, J. Kim, J.A. Swisher, B. Smit, M. Haranczyk., Similarity‐driven discovery of zeolite materials for adsorption‐based separations, ChemPhysChem, 13 (2012) 3595-3597.
- G. M. Maggiora., On outliers and activity cliffs why QSAR often disappoints, Journal of Chemical Information and Modeling, 46 (2006) 1535.
- L. Xue, J. Bajorath., Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening, Combinatorial Chemistry & High Throughput Screening, 3 (2000) 363-372.
- N. M. O’Boyle, R.A. Sayle., Comparing structural fingerprints using a literature-based similarity benchmark, Journal of Cheminformatics, 8 (2016) 1-14.
- D. Rogers, M. Hahn., Extended-connectivity fingerprints, Journal of Chemical Information and Modeling, 50 (2010) 742-754.
- R. Todeschini, V. Consonni, H. Xiang, J. Holliday, M. Buscema, P. Willett., Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets, Journal of Chemical Information and Modeling, 52 (2012) 2884-2901.