In this chapter, we have tried to introduce almost main aspects of QSAR in drug design in an stepwise manner. However, the chapter is not by any means thorough and the readers are encouraged to follow the details of different topics covered here in the supplied references. The field of QSAR studies in drug development is an open area and it seems that the current methods are far from predicting the exact activity of the test compounds. However, as a principle, the prediction methods cannot provide answer more accurate than the experimental data. The maximum accuracy one may expect to achieve from QSAR modeling is in the order of biological data. Therefore, although not an inherent feature of the modeling methods, the improved assay methods can have profound effects on the developed mod­els. One of the main players of the QSAR studies is the way the chemicals are presented numerically by the descriptors. There are huge numbers of descriptors which have been developed to capture the molecular features. In most cases, they are calculated by programs with high speed and are used with­out any pre-judgment in the subsequent steps. But in most instances, the interpretation of the observed activities based on the descriptors is not obvious. Development of new methods for presenting those features of the molecular structures more relevant to drug-likeness may improve the applied aspects of the QSAR modeling efforts. The choice of which algorithm should be used for descriptor selection is also a challenging stage making it a vitally fundamental phenomena for generating QSAR models. The better selection of descriptors, the more reliable the QSAR models. The important parts of every QSAR model generation are the robustness of model prediction and the validity of the generated model. So comparing the results of several types of descriptors through multiple machine learning methods along with cross validating the generated models and testing the data not included in the training set (i.e., the external validation) is an important step for QSAR experiments. The QSAR modeling problems can be related to model prediction and optimization whether on descriptors selection or model generation. Many optimization algorithms have been proposed and employed in computational modeling of drug design. The GA, PSO, ACO, ANN, and variations of them are the most applied algorithms in QSAR studies among which ANN and GA have a strong historical background in pharmaceutical drug design. The ANN as an inspiration of human brain functionality is mostly utilized in model predictions which take effects from many parameters such as feature selection, network topology design, network optimization and validation. From the recent QSAR studies, it can also be deduced that hybrid and ensemble versions of machine learning algorithms as well as graph based machine learning technologies had promising results in comparison with the original artificial intelligence algorithms. However, using the combina­tion of the methods, whether it is in hybrid or ensemble mode, will need a higher computation power to make it possible for huge data sets of pharmaceutical drug industry to be processed. The improved computational algorithms with high efficiency and performance may aid to farther the success rate of QSAR based drug development. The last but not the least, the QSAR models can be improved by utiliz­ing advanced hardware and computational resources.

Ducki, S., Mackenzie, G., Lawrence, N. J., & Snyder, J. P. (2005). Quantitative structure-activity relationship (5D-QSAR) study of combretastatin-like analogues as inhibitors of tubulin assembly. Journal of Medicinal Chemistry, 48(2), 457–465. doi:10.1021/jm049444m PMID:15658859