qsar การใช้

• Second, QSAR models predict the activities of new chemicals.
• The assessment of the reliability of QSAR predictions remains a research topic.
• For example, QSAR models can be used to estimate partition coefficients.
• Development of novel validation parameters for judging quality of QSAR models is also important.
• Pharmacophores are also used as the starting point for developing 3D-QSAR models.
• These QSAR relationships in turn may be used to predict the activity of new analogs.
• Eve automates high-throughput screening, hit confirmation, and QSAR learning and testing.
• QSAR models are differentiated by groups of chemicals characterized by a common mode of action.
• The black box nature of the QSAR model prevents it from addressing these crucial issues.
• QSAR modeling produces predictive lead optimization.
• More recently the first QSAR study concerning antagonists of CXCR3 has been published in the literature.
• A number of related compounds were found to be active allowing a QSAR to be constructed.
• Available computational models provide molecular, thermodynamic, QSAR, atomic, graphical, and spectral properties.
• QSAR models are often used to predict minimum or baseline toxicity of chemicals acting through nonspecific mechanisms.
• QSAR models developed using FATS data are then used to establish computer based systems that predict toxicity.
• MMPA is quite useful in the field of Quantitative structure activity relationship ( QSAR ) modelling studies.
• This problem undermines the applicability of QSAR model in helping the medicinal chemist to make the decision.
• QSAR studies attempt to correlate biological activity of drugs, or a class of drugs, with structures.
• For instance, FATS data is used to develop quantitative structure-activity relationship ( QSAR ) models.
• One of the issues of QSAR models is they are difficult to interpret in a chemically meaningful manner.