Theoretical approaches to drug design and biological activity: critical comments to the use of mathematical methods applied to univariate and multivariate quantitative structure-activity relationships (QSAR)

Smart, Innovative and Intelligent Technologies Used in Drug Designing

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

Structure-neurotoxicity relationships applied to organophosphorus pesticides

Phosphorofluoridates, phosphonofluoridates and phosphorodiamidofluoridates were used as model compound in studying quantitative structure-neurotoxicity relationships. The minimum dose (mg/kg i.m., hen) producing ataxia after 12 days was regressed against physiochemical parameters. It was shown by using the response surface optimization of the MASCA model, that the neurotoxicity depended on lipophilic and steric substituent properties.