COMPUTER AIDED MOLECULAR DESIGN
INTRODUCTION
Human perception has played and still plays a key role in the creation process of finding of new lead structure. This chemical intuition is rooted in a chemist's ability to recognize similarities between structurally different molecules. Where the human ability to recognize pattern is rather powerful and fault tolerance it becomes impossible when the number of compounds and the resulting possible interrelate pattern become very large (P. W. Finn 1999). To support this intuition for hundreds or thousands of compounds multi parametric models for receptor ligand interaction must be created.
Medicinal chemistry
Medicinal or pharmaceutical chemistry is a scientific discipline at the intersection of chemistry and pharmacy involved with designing and developing pharmaceutical drugs. Medicinal chemistry involves the identification, synthesis and development of new chemical entities appropriate for therapeutic use. It also includes the study of existing drugs, their biological properties, and their quantitative structure activity relationship (QSAR). Pharmaceutical chemistry is persistent on quality aspect of medicines and aims to assure fitness for the purpose of medicinal products. Medicinal chemistry is highly interdisciplinary science combining organic chemistry with biochemistry computational chemistry pharmacology statistics and physical chemistry
Process of Drug Discovery
Discovery
The first step of drug discovery involves the identification of new active compounds, often called "hits", which are typically found by screening many compounds for the desired biological properties. These hits can come from natural sources, such as plants, animal or fungi. More often, the hits can come from synthetic sources, such as historical compound collections and combinatorial chemistry. Recent developments in robotics and minimization have greatly accelerate and automated the screening process. Typically, a company will assay over 100,000 individual compounds before moving to the optimization step.
Optimization
The second step of drug discovery involves the synthetic modification of the hits in order to improve the biological properties of the compound pharmacophore. The quantitative structure activity relationship of the pharmacophore play an important part in finding "lead compounds", which demonstrate the most potency, most selectivity, best pharmacokinetics and least toxicity.
Development
The final step involves the rendering the "lead compounds" suitable for use in clinical trials. This involves the optimization of the synthetic route for bulk production, and the preparation of a suitable drug formulation
Cheminformatics used in drug discovery
Cheminformatics (also known as chemoinformatics and chemical informatics) is the use of computer and informational techniques, applied to a range of problems in the field of chemistry. These in silico techniques are used in pharmaceutical companies in the process of drug discovery. The mixing of those information resources (information technology and information management) to renovate data into information and information into knowledge to the intended purpose of making better decision closer in the area of drug lead recognition and optimization. Cheminformatics is applied on different drug designing techniques (Searls, D.B 2000). ?Prediction of physical and chemical properties (QSAR). ?Chemical reaction and synthesis design. ?Prediction of protein structure and function. ?Drug design. ?Virtual screening. ?Insilco database creating searching. ?Molecular docking. ?Molecular descriptors (Similarity, Diversity). ?Rational drug design. Chemoinformatics is generic terms that encompass the design, creation, organization, management, retrieval, analysis0, visualization and use of chemical information
Computational chemistry in silico drug design
Computational chemistry is a branch of chemistry that uses the results of theoretical chemistry integrated into proficient computer programs to calculate the structures and properties of molecules and solids, applying these programs to real chemical problems. Examples of such properties are structure (i.e. the expected positions of the constituent atoms), energy and interaction energy, charges, dipoles and higher multiplepole moments vibrational frequencies, reactivity or the other spectroscopic quantitative, and cross sections for collision with other particles. The term computational chemistry is also sometimes used to cover any of the areas of science that overlap between computer science and chemistry. Electronic configuration theory is the largest sub discipline of computational chemistry(Ulf Madsen 1990). Thus computational chemistry can assist the experimental chemist or it can challenge the experimental chemist to find exclusively new chemical objects (Beroza, P. et al 2002). Several major areas may be distinguished within computational chemistry: The prediction of the molecular structure of molecules by the use of the simulation of forces to find stationary points on the energy hyper surface as the position of the nuclei is varied. Storing and searching for data on chemical entities. Identify relationship among chemical structure and properties. Computational approaches to help in the competent synthesis of compounds. Computational approaches to design molecules that interrelate in specific ways with other molecules (e.g. drug design).
Combinatorial chemistry in drug design
Combinatorial chemistry is one of the important new methodologies developed by academics and researchers in the pharmaceutical, agrochemical, and biotechnology industries to reduce the time and costs associated with produce effectual, profitable, and spirited new drugs. Scientists use combinatorial chemistry to generate huge populations of molecules, or libraries that can be screened economically. By producing larger, additional assorted compound libraries, companies increase the probability that they will discover novel compounds of significant therapeutic and commercial value. The field represents a convergence of chemistry and biology, made probable by essential progress in miniaturization, robotics, and receptor development (Sally Rose ,2003). While combinatorial chemistry can be explained simply, its application can take a diversity of forms, each requiring a complex interchange of classical organic synthesis techniques, rational drug design strategies, robotics, and scientific information management
Virtual Screening in current lead discovery
Virtual screening has become an vital component of the drug discovery process (Jorgensen, W.L. (2004). Its principal aim is the in silico assay of diverse chemical structures for the purposes of establishing their binding affi nities to separate active and inactive molecules or establish their rank order of activity. Virtual screening approaches can use either the 3D structure of the target (target-based virtual screening or docking) or use active and inactive ligands (ligand-based approaches) to determine and rank those structures most likely to bind. In target-based virtual screening, the 3D structure of the target has been previously determined experimentally (usually by X-ray crystallography or NMR) or comes from computational modelling (e.g. based on protein sequence homology to targets with known 3D structures . It usually proceeds in two steps. First, possible conformations and poses of the ligand in the pocket are computationally generated (the docking step) and, second, these are ranked according to how well they fit the pocket (the scoring step). Moreover, results from the scoring step are often applied to compare and rank different chemical structures (called database enrichment or focusing). Scoring functions are applied in all of these stages. In many cases, the same scoring function is used in all steps, although separate functions have increasingly been introduced for these roles. Although it might appear quite natural that combining results from different methods would lead to some improvement in performance, in reality this perception has only recently been applied in drug discovery
High-throughput and virtual screening
High-throughput and virtual screening is important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Diverse statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening area
Computational Approaches to Rational Drug Design
The rational approach to pharmaceutical drug design begins with an investigation of the relation-ship between chemical structure and biological activity. Information gained from this analysis is Used to aid the design of new, or improved, drugs. Primary considerations during this investigation are the geometric and chemical characteristics of the molecules. Computational chemists who are involved in rational drug design routinely use an array of programs to compute, among other things, molecular surfaces and molecular volume, models of receptor sites, dockings of ligands inside protein cavities, and geometric invariants among different molecules that exhibit similar activity. There is a pressing need for efficient and accurate solutions to the above problems. Often, limiting assumptions need to be made, in order to make the calculations tractable. Also, the amount of data processed when searching for a potential drug is currently very large and is only expected to grow larger in the future
De novo Drug design
One of the biggest challenges facing today's pharmaceutical industry is the discovery of new drugs, with economic pressures driving for a quicker turn around time. Most drugs are designed by either modifying the structure of known drugs, by screening compound libraries using disease models, or by developing proteins as therapeutic agents or vaccines. Newer methods involve computerized methods to identify/design de novo compounds that modify the activity of a target protein. De novo design involves creating new molecules from scratch. This is a daunting task, as the search space of feasible structures is of the order of 10100. The core principles of de novo design involve not only assembling possible compounds and evaluating their quality but also searching the sample space for novel structures with drug-like properties. One of the first steps of the design process obviously involves reducing the search space to a more manageable size, but even then a comprehensive analysis is usually not feasible. Once target compounds (hits) have been generated, the next step, which is crucial in the design process, is to assay or score them to judge which ones are the most promising. This scoring process is usually dynamic and runs simultaneously with the generation and evolution of the hit, guiding the process to the end product. This is the ligand optimization approach by using the analysis of protein active site properties which can be possible contact area by ligand
Computer Aided Drug Design
Drug research and development (R & D) is comprehensive, expensive, time-consuming and full of risk. It is estimated that a drug from concept to market would take 12 years and cost more than US$800 million on an average. Several new technologies have hence been developed and applied in drug R & D to shorten the research cycle and to reduce the expenses. Computer-aided drug design (CADD) is one of such evolutionary technologies. Having emerged as a quantitative structureactivity relationship (QSAR) analysis in the early 1960s, the concept of CADD has evolved very quickly, especially in the recent decade as an unprecedented development of structural biology and computer capabilities. CADD technologies including molecular modelling and simulation have become promising in drug discovery. Recently, CADD has even been used in designing highly selective ligands for a certain target that shares very similar structures with many proteins, which is difficult to be done by other methods. CADD tools have been applied in almost every stage of drug R & D, greatly changing the strategy and pipe-line for drug discovery. CADD, from its traditional application of lead discovery and optimization, has extended toward two directions: upstream for target identification and validation, and downstream for preclinical study (ADMET prediction). In this review, we highlight some recent advances of CADD technologies; emphases are put on computational tools for target identification and new chemical entity discovery
We provide a professional essay writing service that thousands of our customers use as an effective way of improving their grades, improving their research and saving them lots of time.
