Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species

• Published in: Analytica chimica acta Vol. 651; no. 2; pp. 159 - 164
• Main Authors: Prado-Prado, Francisco J., Borges, Fernanda, Uriarte, Eugenio, Peréz-Montoto, Lazaro G., González-Díaz, Humberto
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 05.10.2009; Elsevier
• Subjects: Antiviral Agents - chemistry; Antiviral Agents - pharmacology; Antiviral drugs; Biological and medical sciences; Databases, Factual; Discriminant Analysis; General pharmacology; Linear Discriminant Analysis; Markov Chains; Markov model; Medical sciences; Multi-target Quantitative Structure–Activity Relationship; Pharmacology. Drug treatments; Physicochemical properties. Structure-activity relationships; Quantitative Structure-Activity Relationship; Spectral moments; Viruses - drug effects; Spectral moments; Markov model; Multi-target Quantitative Structure–Activity Relationship; Antiviral drugs; Linear Discriminant Analysis; Drug; Discriminant analysis; Molecular structure; Structure-Activity Relationship; Multi-target Quantitative; Biological activity; Spectral analysis; Markov chain; Structure activity relation; Database; Antiviral
• ISSN: 0003-2670; 1873-4324
• DOI: 10.1016/j.aca.2009.08.022

The antiviral QSAR models have an important limitation today. They predict the biological activity of drugs against only one viral species. This is determined by the fact that most of the current reported molecular descriptors encode only information about the molecular structure. As a result, predicting the probability with which a drug is active against different viral species with a single unifying model is a goal of major importance. In this work, we use Markov Chain theory to calculate new multi-target spectral moments to fit a QSAR model for drugs active against 40 viral species. The model is based on 500 drugs (including active and non-active compounds) tested as antiviral agents in the recent literature; not all drugs were predicted against all viruses, but only those with experimental values. The database also contains 207 well-known compounds (not as recent as the previous ones) reported in the Merck Index with other activities that do not include antiviral action against any virus species. We used Linear Discriminant Analysis (LDA) to classify all these drugs into two classes as active or non-active against the different viral species tested, whose data we processed. The model correctly classifies 5129 out of 5594 non-active compounds (91.69%) and 412 out of 422 active compounds (97.63%). Overall training predictability was 92.34%. The validation of the model was carried out by means of external predicting series, the model classifying, thus, 2568 out of 2779 non-active compounds and 224 out of 229 active compounds. Overall training predictability was 92.82%. The present work reports the first attempts to calculate within a unified framework the probabilities of antiviral drugs against different virus species based on a spectral moment analysis.