Quantitative Structure Activity Relationship for Drug Discovery
Uppaluru Himaja Sree1, Dandabathula Kumudhini2, Vuyyuru Sruthi Laya3, Guttikonda Geetha4
1Uppaluru Himaja Sree, Information Technology, Velagapudi Rama Siddhartha, Vijayawada, India.
2Dandabathula Kumudhini,, Information Technology, Velagapudi Rama Siddhartha, Vijayawada, India.
3Vuyyuru Sruthi Laya, Information Technology, Velagapudi Rama Siddhartha, Vijayawada, India.
4Guttikonda Geetha, Assistant Professor, Information Technology, VR Siddhartha, Vijayawada, India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 607-610 | Volume-8 Issue-9S August 2019 | Retrieval Number: I10960789S19/19©BEIESP | DOI: 10.35940/ijitee.I1096.0789S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Quantitative structure-activity relationship (QSAR), gives useful information for drug design and medicinal chemistry. QSAR is a method used to anticipate the organic reaction of a molecule by developing equations which use descriptors calculated from its compounds. The molecular descriptors vary in complexity. A time consuming and expensive process for pharmaceutical industries is drug discovery. An inspiration driving these QSAR models is to help revive the revelation of molecular drug candidates through minimized test work and to bring a drug to market faster. To obtain sorted features principal component analysis is used. The biological activities of the test set are determined by training the neural network using training set. By predicting the activities it can be known whether the drug is close to the target or not.
Keywords: Biological Activity, Descriptors, Neural Networks, Quantitative- Structure Activity Relationship.
Scope of the Article: Soil-Structure Interaction