Computational chemistry is the application of chemical, mathematical, and computing skills to the solution of interesting chemical problems. It uses computer to generate information such as properties of molecules or simulated experiment al results. Very few aspects of chemistry can be computed exactly but almost every aspects of chemistry has been described in a qualitative or approximate quantitative computational scheme. The biggest mistake that computational chemists can make is to assume that any computed number is exact. However, just as not all spectra are perfectly resolved, often a quantitative or approximate computation can give useful insight into chemistry if you understand what it tells you and what it doesn’t. Computational chemistry has become a useful way to investigate materials that are not too difficult to find or expensive to purchase. It also helps chemists make predictions before running the actual experiments so that they can better be prepared for making observations.
The incidence of opportunistic fungal infections has increased dramatically with increasing number of immune suppressed patients. Moreover, fungal pathogens started to develop resistance to commonly used anti fungal chemotherapeutic agents. Fungal infection has great concern since it is implicated in Tuberculosis, Cancer and AIDS. Although, there are newer, less toxic anti fungal agents available for clinical use but their clinical efficacy is not active against various fungal infections. So there is a constant need for the discovery of anti fungal drugs
Candida albicans is one of the many huge bugs which is set to be found living in and on all us. It is a fungus organism best known for causing thrush in the mouth of babies, moist plaques in the mouth and on the tongue, it can also cause nappy rash, soreness and itching around the anus and genitals in adults. Candida albicans is an opportunistic fungus that is the cause of many undesirable symptoms raising from fatigue and weight gain, to joint pain and gassing. The number of clinical infections worldwide by Candida albicans has risen considerably in recent years and the incidence of resistance to traditional antifungal therapies is also increasing. In addition, drug related toxicity, significant drug interactions and insufficient bioavailability of the conventional anti fungal, have encouraged the search for new alternative among natural products. computer aided drug designing depends on the accuracy of modeled structure, accuracy of tools used for predicting binding site, performance of docking algorithms, correctness in mapping the pharmacophore, accuracy of energy minimization and simulation algorithms, realiability of ADME and toxicity prediction tools as well as on synthetic feasibility of designed inhibitor, selection of drug target also decides the success and failure of drug disease.
The quantitative structure–activity relationship (QSAR) approach helps to correlate the specific biological activities or physical properties of a series of compounds with the measured or computed molecular properties of the compounds, in terms of descriptors. QSAR methodologies save resources and hasten the process of the development of new molecules and drugs. There have been many QSAR researches related to design of anti-Candida albicans drugs so far but a systematic QSAR study is to be carried out for series of ketones and tetra ketones derivatives carrying a branched one to three amino functions. The aim of present work is to derive some statistically significant QSAR models for side chain modified ketones and tetra ketones derivatives for their anti-Candida albicans activities and to relate anti-Candida albicans activity to its physicochemical properties
Computer aided drug design (CADD) and delivery systems offers an in depth discussion of the computer assisted techniques used to discover, drug and optimize new effects and safe drugs. The objective of drug design is to find a chemical compound that can fit to a specific cavity on a protein target both geometrically and chemically. It is generally discovered that drug discovery and development are very time and resources consuming processes. There is an ever grow in effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. Accuracy of these predictive models can be further improved by adding more reliable data and significant parameters related to toxicity. There is need to design a high quality database for drug designing that should contain information about mechanism of a specific disease, potential drug targets, natural leads, physiochemical properties, pharmacophore , QSAR models, previous efforts made in drug discovery , clinical trial data, efficacy and side effects. Drug designing is an art, but the application of computer will play a major role in the attempts to make it more rational and successful in the future. Extensive use of computational approaches with higher accuracy could reduce the overall cost and failure of drug designing.
1.1 AIM AND OBJECTIVES
The aim of this project is to develop a good and rational Quantitative Structure Activity Relationship mathematical models that can predict to a significant level anti Candida albicans Minimum Inhibitory Concentration (MIC) ketone and tetra-ketone derivatives.
- To use density functional theory for the calculation of molecular descriptors that describe anti-canida albicans.
- To use molecular descriptors to develop quantitative structure activity relationship (QSAR) model which reveal the biological activities of the studied molecules.