Advances in computer aided drug design

Advances in computer aided drug design

• 1.
  AADDVVAANNCCEESS IINN CCOOMMPPUUTTEERR--AAIIDDEEDDDDRRUUGG DDEESSIIGGNN The ultimate goal of developing drugs that target a specific cellular protein is to find compounds or ligands that bind to the protein and modulate its function. The process typically progresses from the initial discovery of hits with weak to moderate potency against the target protein to the final optimized and highly potent compounds suitable for clinical trials. to achieve this goal, computational methods have been applied at various stages in the process that can be classified into four major areas: 1) Virtual focused library design and ADMET profiling. 2) Protein structure preparation: Identify and generate models of the target protein structure. (ie. x-ray crystallography, NMR spectroscopy and homology modelling) 3) Ligand structure preparation: Identify and generate models of candidate ligands that may be available for experimental validation. (ie. 3D pharmacophore generation and scaffold hopping) 4) Docking: Identify and generate the structure of the complex formed by the protein and the ligand. (ie. Quantitative structure-activity relationship (QSAR) and high-throughput screening.) 5) Scoring: Estimate or predict the binding affinity of the ligand for the protein. where: desolvation – enthalpic penalty for removing the ligand from solvent. motion – entropic penalty for reducing the degrees of freedom when a ligand binds to its receptor. configuration – conformational strain energy required to put the ligand in its "active" conformation. interaction – enthalpic gain for "resolvating" the ligand with its receptor. Since the first reported success of discovery by design over three decades ago, there has been an explosion in the number, variety and sophistication of resources and analysis tools. CADD is now widely recognized as a viable alternative and complement to high-throughput screening. The search for new molecular entities has led to the construction of high quality datasets and design libraries that may be optimized for molecular diversity or similarity. On the other hand, advances in molecular docking algorithms, combined with improvements in computational infrastructure, are enabling rapid improvement in screening throughput. Propelled by increasingly powerful technology, distributed computing is gaining popularity for large-scale screening initiatives. 1) Numerous bioinformatics tools and resources have been developed to expedite drug discovery process. CADD provides an overview of the most important data sources and computational methods for the discovery of new molecular entities alongwith the guidelines on the workflow of the entire virtual screening campaign, from data collection through to postscreening analysis. 2) Data accessibility is critical for the success of a drug discovery and development campaign. Smallmolecule databases represent a major resource for the study of biochemical interactions. Biological databases represent current accumulated knowledge on human biology and disease. Combinatorial libraries allow for optimization of a library’s diversity or similarity to a target and can help minimize redundancy or maximize the number of discovered true leads. 3) A campaign usually begins with the selection of biological targets whose role in the disease pathway is established. Next, it is necessary to prepare the library of compounds to be screened. For a target protein of unknown structure, a 3D model may be constructed using "homology modeling" techniques. This is usually followed by protonation of the target protein, energy minimization, molecular docking and stereochemical quality assessments. 4) The European Union funded WISDOM (World-wide In Silico Docking on Malaria) project which analyzed over 41 million malaria-relevant compounds in 01 month using 1700 computers from 15 countries. 5) The Chinese funded Drug Discovery Grid (DDGrid) for anti-SARS and anti-diabetes research with a calculation capacity of >1 Tflops per second. 6) Numerous successes of designed drugs were reported, including Dorzolamide for the treatment of cystoid macular edema, Zanamivir for therapeutic or prophylactic treatment of influenza infection, Sildenafil for the treatment of male erectile dysfunction, and Amprenavir for the treatment of HIV infection. • Introduction of new therapeutic solutions is an expensive and time-consuming process. It is estimated that a typical drug discovery cycle, from lead identification through to clinical trials, can take 14 years with cost of 800 million US dollars. In the early 1990s, rapid developments in the fields of combinatorial chemistry and high-throughput screening technologies have created an environment for expediting the discovery process by enabling huge libraries of compounds to be synthesized and screened in short periods of time. However, many of these identified trials failed to be further optimized into actual leads and preclinical, in which 40–60% was reported due to absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) deficiencies. Collectively, these issues underscore the need to develop alternative strategies. • In time, a new paradigm in drug discovery came underway, which have early assessment of activity and their potential ADME/Tox liabilities. This helps reduce costly late-stage failures and accelerates successful development of new molecular entities with the application of computational techniques. Computer-aided drug design (CADD) is a widely-used term that represents computational tools and resources for the storage, management, analysis and modeling of compounds. It includes development of digital repositories for the study of chemical interaction relationship, computer programs for designing compounds with interesting physicochemical characteristics, as well as tools for systematic assessment before the synthesis and testing of compounds. • The more recent foundations of CADD were established in the early 1970s with the use of structural biology to modify the biological activity of insulin and to guide the synthesis of human haemoglobin ligands. At that time, X-ray crystallography was expensive and time-consuming, rendering it infeasible for large-scale screening in industrial laboratories. • Over the years, new technologies such as comparative modeling based on natural structural homologues have emerged and began to be exploited. These, together with advances in combinatorial chemistry, high-throughput screening technologies and computational infrastructures, have rapidly bridged the gap between theoretical modeling and medicinal chemistry. • Numerous successes of designed drugs were reported, including Dorzolamide, Zanamivir, Sildenafil and Amprenavir. •CADD now plays a critical role in the search for new molecular entities. Current focus includes improved design and management of data sources, creation of computer programs to generate huge libraries of pharmacologically interesting compounds, development of new algorithms to assess the potency and selectivity of lead candidates, and design of predictive tools to identify potential ADME/Tox liabilities. Soni Vikas, Tripathy Richa Dept. of Pharmaceutical Sciences, Dr. H.S. Gour Central University, Sagar, M.P. Modern drug discovery is characterized by the production of vast quantities of compounds & the need to examine these huge libraries in short periods of time. The need to store, manage & analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design & discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reaction capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources & computational methods for the discovery of new molecular entities. The workflow of entire virtual screening campaign is discussed, from data collection through to post-screening analysis. Keywords: computer-aided drug design, virtual screening, computational modeling. ^Beale J. M., Jr. Wilson C. O.; Wilson and Gisvold's Textbook of Organic Medicinal and Pharmaceutical Chemistry, 12th edition,2011; Wolters Kluwer Health/Lippincott Williams & Wilkins: Philadelphia, PA: 17-40. ^Greer J, Erickson JW, Baldwin JJ, Varney MD ; "Application of the three-dimensional structures of protein target molecules in structure-based drug design",(April 1994) Journal of Medicinal Chemistry 37 (8): 1035–54. ^Hann MM, Leach AR, Harper G. Molecular complexity and its impact on the probability of finding leads for drug discovery. J Chem Inf Comput Sci 2001;41:856–64. ^Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery: Part 1. A qualitative and quantitative characterization of known drug databases. JCombChem 1999;1:55–68. AABBSSTTRRAACCTT RREEFFEERREENNCCEESS IINNTTRROODDUUCCTTIIOONN EEXXPPEERRIIMMEENNTTAALL SSEECCTTIIOONN RREESSUULLTT && DDIISSCCUUSSSSIIOONN