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Introduction High throughput screening technology has been improved by the assistance of numerous other technologies including the combinatorial and computational approaches. QSAR is one of the computational tools which facilitate the process of hit to lead generation in HTS. Quantitative structure-activity relationship (QSAR) helps to view the structure of compounds in the context of their possible activities or functions. QSAR is a tailor-made HTS strategy which helps to improve the overall process of screening compound libraries. History of QSAR History of QSAR dates back to 19th century when it was observed that toxicity of alcohols increased in mammals as water solubility of alcohols decreased [1]. Later on, it was also found that lipophilicity increases the toxicity of organic compounds [2]. In 1964, Hansch and Fujita developed the technique of QSAR. IN 1969 Hansch developed an equation that related biological activity to electronic properties and hydrophobicity of some structures. log (1/C) = k1log P – k2(log P)2 + k3? + k4 where, C = minimum effective dose P = octanol – water partition coefficient ? = Hammett substituent constant kx= constants derived from regression analysis Log P tells about the drug’s ability to pass through cell membranes. P is the partition coefficient of octanol which represents biological membrane and water which reflects the fluid in cells and blood. On the other hand the Hammett substituent constant (?) is an index to molecule’s intrinsic reactivity caused by aryl substituents. If the structure under observation has an aromatic ring, the rate of reaction can change up to six orders of magnitude. Therefore, it becomes clear how log P and Hammett substituent in the given mathematical equation relates to the solubility of a particular structure and how the solubility of related structure can be predicted by this mathematical model. How to Make a Computer Understand? In order to use QSAR approach in HTS, computers are taught about the descriptors or certain structures and the particular activity associated with them. This is a very interesting process and computers can learn just as a researcher or scientist learns. ”" Computers cannot recognize the chemical structures. The structures have to be changed to numbers which are then fed to neural networks for machine learning. All the data that is fed to the softwares for its learning is called as training data. For example, it is told to the software that structure X imparts hydrophobicity and software is designed with this information. Once the software learns, experimental data can be added to the software for making prediction models. Next time, whenever a new compound with structure X or its analog will be tested with the software, the software will predict it to be hydrophobic. Similarly the software can be trained for active sites of different targets and any experimental compound having the amino acids complementary to the active site will be predicted as potent inhibitors by the software. This is how, kinase inhibitors, apoptosis inhibitors, cdk inhibiotrs etc. can be predicted by exploiting QSAR. Development of Anticancer Drugs with QSAR Various studies have reported the development of neural networks which can predict the compounds to be anticancer or non-anticancer in nature. This is a valuable progress in facilitating the HTS assays for the development of cancer drugs. In a study 122 non-redundant anticancer (e.g. hepsulfam, vincristine sulfate, taxol or paclitaxel etc.) and 258 non-redundant non-anticancer drugs were used to build an artificial neural network to predict in future that whether an inhibitor can act as an anti-cancer drug or not [3]. Conclusion QSAR is an attention-grabbing, fascinating and promising approach. It can transfigure the HTS far beyond its existing state. Although it takes time to train a computer with the structures and develop software but once it is developed successfully, it proves to be helpful for millions of researchers. Also, already developed softwares can also be utilized by the large pharmaceutical companies to predict the models. There is no doubt in saying that QSAR is a revolutionizing technology that is worth studying and worth implicating in HTS! References 1. Borman, S. (1990) New QSAR Techniques Eyed for Environmental Assessments. Chem. Eng. News, 68: 20-23. 2. Lipnick, R.L. (1986) Charles Ernest Overton: Narcosis Studies and a Contribution to General Pharmacology. Trends Pharmacol. Sci., 7: 161-164. 3. Jaiswal K, Naik PK et al. Distinguishing compounds with anticancer activity by ANN using inductive QSAR descriptors. Bioinformation 2008; 2(10): 441–451. Related Posts Computational Approach fro HTS: Q(SAR) Rational HTS for Cancer Research Related to Role of QSAR in HTS Rational High Throughput Screening Introduction Modern trends towards drug discovery involve the cherry picking of inhibitors from compound libraries to screen millions of samples. The number of compounds in ... Computational Approach for HTS: Q(SAR) Introduction High throughput screening has become the leading tool in drug discovery. With its fundamentals of sample collection with subsequent screening to find hits to ... Virtual Screening and HTS In this day and age, computers have taken lead in each and every pitch of life. Most of the technologies discovered so far are indebted ... 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