Study of physicochemical properties-antitubercular activity relationship of naphthalene-1,4-dione analogs: A QSAR approach

Abstract

Enduring pandemic nature of tuberculosis urges to explore novel antimycobacterial agents with different mechanism of actions. Plant containing 1,4-naphthoquinone derivatives showed promising antitubercular activity with novel mode of action and their synthetic/semi-synthetic derivatives devoid of toxicity. Structural requirements of naphthalene-1,4-dione derivatives towards antitubercular activity and cytotoxicity were explored using a mathematical model in order to understand their physicochemical parameters. A quantitative structure activity relationship study furnished some important structural insights for antitubercular activity, i.e. average connectivity index, information content index of neighborhood symmetry, aromatic ratio, and path/walk 3 Randic shape index. Similarly it also provides drivers for the cytotoxicity of compounds. These drivers will be helpful in further development of safer and potent 1,4-naphthoquinone derivatives.

Multivariate SAR and QSAR of cucurbitacin derivatives as cytotoxic compounds in a human lung adenocarcinoma cell line

“…An increase in this value tends to reduce the potential for activity. According to Gupta et al [70], increases in the average connectivity indices are related to branching. Thus, multi-branched molecules will have difficulty producing cytotoxicity.…”

QSAR Studies and Structure Property/Activity Relationships Applied in Pyrazine Derivatives as Antiproliferative Agents Against the BGC823

“…8 Density functional theory methods [9][10][11][12][13][14] offer an alternative use of inexpensive computational methods which could handle relatively large molecules. [15][16][17][18][19][20] Quantitative structure-activity relationships (QSAR) [21][22][23][24][25] are attempts to correlate molecular structure, or properties derived from molecular structure, with a particular kind of chemical or biochemical activity. The kind of activity is a function of the interest of the user.…”

Predicting network of drug–enzyme interaction based on machine learning method

“…In this study, we improved representation of drugs by using physicochemical properties which are widely used in SAR/QSAR and drug design [21][22][23][24][25][26][27]. Then, we applied pseudo amino acid composition to encode enzymes.…”