Computer Aided Drug design

Computer Aided Drug design

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Downloads: 0 - 10 Saturday 16th December 2017 Report

Computer Aided Drug design - Overview

------------ PAGE 1 ------------ Computer- Aided Drug Designing ( CADD) Md. Khalidur Rahman ( 161- 15- 7131) Niloy Sarker ( 161- 15- 7372) M. Mohiminur Rahaman ( 152- 15- 5694) ------------ PAGE 2 ------------ Bioinformatics o An application of Computer Science to biological and Drug Development science o Bioinformatics is the field of science in which biology, computer science, and information technology merge to form a single discipline o The ultimate goal of the field is to enable the discovery of new biological insights ------------ PAGE 3 ------------ Classification ------------ PAGE 4 ------------ Computer- Aided Drug Designing ( CADD) o Computer- Aided Drug Designing ( CADD) is a specialized discipline that uses computational methods to simulate drug- receptor interactions o CADD methods are heavily dependent on bioinformatics tools, applications and databases ------------ PAGE 5 ------------ Drug Discovery and Development Identify disease Find a drug effective against disease protein ( 2- 5 years) Isolate protein involved in disease ( 2- 5 years) Preclinical testing ( 1- 3 years) Formulation and Scale- up Human clinical trials ( 2- 10 years) FDA approval ( 2- 3 years) ------------ PAGE 6 ------------ Bioinformatics Supports CADD Research Virtual High- Throughput Screening ( vHTS) Sequence Analysis Homology Modeling Similarity Searches Drug Lead Optimization Physicochemical Modeling Drug Bioavailability and Bioactivity ------------ PAGE 7 ------------ Virtual High- Throughput Screening ( vHTS) o The protein targets are screened against databases of small- molecule compounds o With today’s computational resources, several million compounds can be screened in a few days on sufficiently large clustered computers o This method provides a handful of promising leads e. g. ZINC is a good example of a vHTS compound library ------------ PAGE 8 ------------ Sequence Analysis o It is very useful to determine how similar or dissimilar the organisms are based on gene or protein sequences o With this information one can infer the evolutionary relationships of the organisms o There are many bioinformatic sequence analysis tools that can be used to determine the level of sequence similarity e. g. DNA sequence analysis, gel electrophoresis ------------ PAGE 9 ------------ Homology Modeling o A common challenge in CADD research is determining the 3- D structure of proteins o The 3- D structure for only a small fraction of the proteins is known o Bioinformatics software tools are then used to predict the 3- D structure of the target based on the known 3- D structures of the templates o E. g. MODELLER SWISS- MODEL Repository ------------ PAGE 10 ------------ Similarity Searches o A common activity in biopharmaceutical companies is the search for drug analogues o Starting with a promising drug molecule, one can search for chemical compounds with similar structure or properties to a known compound o A variety of bioinformatic tools and search engines are available for this work ------------ PAGE 11 ------------ Benefits of CADD o The Tufts Report suggests that the cost of drug discovery and development has reached $ 800 million for each drug successfully brought to market o Many biopharmaceutical companies now use computational methods and bioinformatics tools to reduce this cost burden ------------ PAGE 12 ------------ Benefits of CADD o Virtual screening, lead optimization and predictions of bioavailability and bioactivity can help guide experimental research o Only the most promising experimental lines of inquiry can be followed and experimental dead- ends can be avoided early based on the results of CADD simulations ------------ PAGE 13 ------------ Benefits of CADD Time- to- Market: o CADD has predictive power o It focuses drug research on specific lead candidates and avoids potential “ dead - end” compounds ------------ PAGE 14 ------------ Benefits of CADD Insight: o CADD provides a deep insight to the drug- receptor interactions acquired by the researchers o Molecular models of drug compounds can reveal intricate, atomic scale binding properties that are difficult to envision in any other way ------------ PAGE 15 ------------ Identify disease Isolate protein Find drug Preclinical testing GENOMICS, PROTEOMICS and BIOPHARM. Potentially producing many more targets and “ personalized” targets HIGH THROUGHPUT SCREENING Screening up to 100,000 compounds a day for activity against a target protein VIRTUAL SCREENING Using a computer to predict activity COMBINATORIAL CHEMISTRY Rapidly producing vast numbers of compounds MOLECULAR MODELING Computer graphics and models help improve activity IN- VITRO and IN- SILICO ADME MODELS Tissue and computer models begin to replace animal testing ------------ PAGE 16 ------------ CADD and bioinformatics together are a powerful combination in drug research and development. ------------ PAGE 17 ------------ Research Achievements o Software developed o Bioinformatics database developed ------------ PAGE 18 ------------ developed
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