Design Hub for Drug Discovery Drug Design – Decision Making

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Akos Tarcsay DESIGN HUB FOR DRUG DISCOVERY DRUG DESIGN – DECISION MAKING Identify the goal Gather information Identify alternatives Weigh the evidence Choose among alternatives Take action Review your decision DRUG DESIGN – DECISION MAKING Identify the goal Gather information Identify alternatives Weigh the evidence Choose among alternatives Take action Review your decision Models Variations Information Data Analysis Idea Brainstorming Plugin results repository space DRUG DESIGN – DECISION MAKING Identify the goal Gather information Identify alternatives Weigh the evidence Choose among alternatives Take action Review your decision GATHER INFORMATION: PLUGIN SYSTEM Service-agnostic Real-time Dynamic Easy to extend As you draw Right amount of information (REST, SQL, CLI) GATHER INFORMATION Array of services - Data access - Preprocessed data MMPA - Derived models QSAR AI, Machine Learning GATHER INFORMATION Service-agnostic integration Marvin Live GATHER INFORMATION Dynamic Marvin Live GATHER INFORMATION Dynamic Marvin Live GATHER INFORMATION Data Curation Magic Invoke Plugin GATHER INFORMATION: MATCHED MOLECULAR PAIR ANALYSIS J. Chem. Inf. Model. 2010, 50, 1872–1886 GATHER INFORMATION Data Curation Magic Invoke Plugin ChEMBL Filters MMPDB JChemWS Plugin GATHER INFORMATION Data Curation Magic Invoke Plugin ChEMBL Filters MMPDB JChemWS Plugin ChEMBL23 hERG RDKit MMPDB Import NodeJS Drug-like Hussain and Rea JWS search Standardization SQLite DB GATHER INFORMATION Data Curation Magic Invoke Plugin ChEMBL Filters MMPDB JChemWS Plugin ChEMBL23 hERG RDKit MMPDB Import NodeJS Drug-like Hussain and Rea JWS search Standardization SQLite DB ~1 day GATHER INFORMATION Data Curation Magic Invoke Plugin ChEMBL Filters MMPDB JChemWS Plugin ChEMBL23 hERG RDKit MMPDB Import NodeJS Drug-like Hussain and Rea JWS search Standardization SQLite DB ~1 day MMP CASE STUDY ChEMBL Filters MMPDB JChemWS Plugin DRUG DESIGN – DECISION MAKING Identify the goal Gather information Identify alternatives Weigh the evidence Choose among alternatives Take action Review your decision DRUG DESIGN – DECISION MAKING Identify the goal Gather information Identify alternatives Weigh the evidence Choose among alternatives Take action Review your decision WEIGH THE EVIDENCE CHOOSE AMONG ALTERNATIVES DESIGN HUB: MARVIN LIVE ROADMAP Identify the goal Gather information Identify alternatives Weigh the evidence Choose among alternatives Take action Review your decision DESIGN HUB: MARVIN LIVE ROADMAP Thank you.

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1 Drug Development Working Group Report: Analysis of Existing
Drug Development Working Group Report: Analysis of Existing Strengths, Critical Gaps, and Opportunities for Collaboration May 2014 1. Analysis. Biopharma - Focused on innovation with clinical application. Universities will play an increasingly large role in driving innovation in the biomedical industry for the foreseeable future. In New Jersey, there are several major biopharmaceutical and >350 smaller biotechnology and related companies with one or more employees. The recent integration of Rutgers creates a unique opportunity to align the University’s considerable drug discovery and development strengths. A directed effort of this sort would greatly enhance the capabilities of the University and enable University research to synergize with the biopharma industry, broadly defined. Although there have been shifts, mergers, and acquisitions in the New Jersey biopharma industry, the biopharma industry has continued to thrive. In 2003, the NY/NJ biopharma region had 17 companies with valuation >$100 M; in 2013 this region had still had 16 such companies. Moreover, the region is still vibrant: it continues to be home to large biomedical corporations, startups, entrepreneurs, and the world center for finance and capital. New Jersey also has quality industrial laboratory space, an ecosystem populated by a talented and experienced work force that has made New Jersey the “medicine chest of the world,” and an unparalleled opportunity to combine these factors in biopharma ventures that build on partnerships between Rutgers University and industry. Indeed, the primary difference between now and 15 years ago is that the present industry now is willing to reach out in a highly dynamic fashion. Rutgers is uniquely positioned to step forward as the premier university leader in biomedical innovation and development in the region.
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Drug Design Project Tips
Drug Design Project Information and Tips CHEM 162B (2012) Part of your grade in Drug Design courses will come from a development of a “Drug Design Project”. Students who have taken Chem162A will be able to continue their project development; students who have not taken Chem 162A can pick one of the projects developed in past and take it further. Students will present their projects during the annual Drug Design Poster Event. Students taking Chem 162 during the Winter 2011 will be able accomplish three important milestones toward completing the Project. Part of your grade will be based on your success in completing these milestones. The milestones that you are expected to complete during the Spring 2012 are: 1) Characterize a validated protein/nucleic acid target for the disease that you are working on. If the target is an enzyme, describe in detail the reaction it catalyzes. If the target is not an enzyme, describe in detail its physiological role and mode of operation 2) Obtain or create a virtual library of possible drug candidates that are expected to bind to this target and carry out virtual screening to identify best binders 3) Describe adsorption, distribution, metabolism, excretion, and possible toxicity aspects of your drug candidate(s). If appropriate, propose modifications to improve your drug. You are expected to turn in your work on each milestone by the due dates specified; you’ll automatically receive 3 points per each milestone if you submit a significantly developed work by the due date. You will earn extra 5 points if you submit all milestones by the due date.
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Computational Approaches for Drug Design and Discovery: an Overview
Review Article Computational Approaches for Drug Design and Discovery: An Overview Baldi A College of Pharmacy, Dr. Shri R.M.S. Institute of Science & Technology, Bhanpura, Dist. Mandsaur (M.P.), India ARTICLE INFO ABSTRACT Article history: The process of drug discovery is very complex and requires an interdisciplinary effort to design Received 12 July 2009 effective and commercially feasible drugs. The objective of drug design is to find a chemical Accepted 21 July 2009 compound that can fit to a specific cavity on a protein target both geometrically and chemically. Available online 04 February 2010 After passing the animal tests and human clinical trials, this compound becomes a drug available Keywords: to patients. The conventional drug design methods include random screening of chemicals Computer-aided drug design found in nature or synthesized in laboratories. The problems with this method are long design Combinatorial chemistry cycle and high cost. Modern approach including structure-based drug design with the help Drug of informatic technologies and computational methods has speeded up the drug discovery Structure-based drug deign process in an efficient manner. Remarkable progress has been made during the past five years in almost all the areas concerned with drug design and discovery. An improved generation of softwares with easy operation and superior computational tools to generate chemically stable and worthy compounds with refinement capability has been developed. These tools can tap into cheminformation to shorten the cycle of drug discovery, and thus make drug discovery more cost-effective. A complete overview of drug discovery process with comparison of conventional approaches of drug discovery is discussed here.
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Recent Advances in Automated Protein Design and Its Future
EXPERT OPINION ON DRUG DISCOVERY 2018, VOL. 13, NO. 7, 587–604 https://doi.org/10.1080/17460441.2018.1465922 REVIEW Recent advances in automated protein design and its future challenges Dani Setiawana, Jeffrey Brenderb and Yang Zhanga,c aDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; bRadiation Biology Branch, Center for Cancer Research, National Cancer Institute – NIH, Bethesda, MD, USA; cDepartment of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA ABSTRACT ARTICLE HISTORY Introduction: Protein function is determined by protein structure which is in turn determined by the Received 25 October 2017 corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are Accepted 13 April 2018 understood, it should be possible to refine or even redefine the function of a protein by working KEYWORDS backwards from the desired structure to the sequence. Automated protein design attempts to calculate Protein design; automated the effects of mutations computationally with the goal of more radical or complex transformations than protein design; ab initio are accessible by experimental techniques. design; scoring function; Areas covered: The authors give a brief overview of the recent methodological advances in computer- protein folding; aided protein design, showing how methodological choices affect final design and how automated conformational search protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, parti- cularly in cases where screening by combinatorial mutagenesis is problematic.
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Protein Structure Analysis: Introducing Students to Rational Drug Design
INQUIRY & Protein Structure Analysis: INVESTIGATION Introducing Students to Rational Drug Design • AGNIESZKA SZARECKA, CHRISTOPHER DOBSON ABSTRACT the steps involved in computational drug-design pipelines, a process We describe a series of engaging exercises in which students emulate the process that uses structural data for a specific protein target to rationally that researchers use to efficiently develop new pharmaceutical drugs, that of design the best effector molecule – a drug candidate. rational drug design. The activities are taken from a three- to four-hour Unlike most wet-lab experiments in molecular biology and bio- workshop regularly conducted with first-year college students and presented chemistry, these computational experiments do not require expen- here to take place over three to four class periods. Although targeted at college sive equipment or laboratory setups. The tools we describe here students, these activities may be appropriate at the high school level as well, are predominantly free online resources that are accessible via stan- particularly in an AP Biology course. The exercises introduce students to the dard Internet connection. Locally run software can also be used, topics of bioinformatics and computer modeling, in the context of rational drug design, using free online resources such as databases and computer programs. and we will recommend free and easy-to-install programs, capable Through the process of learning about computational drug design and drug of running on the majority of common platforms. The exercises optimization, students also learn content such as elements of protein structure can easily be divided between pre-lab, in-class, and homework and protein–ligand interactions.
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Kinase Inhibitor Chemistry Jack W
Final Agenda COVER CAMBRIDGE HEALTHTECH INStitute’s eigHTH ANNUAL CONFERENCe-AT-A-GlANCE AND SPONSORS RECORD ATTENDANCE Drug Discovery EXPECTED THIS YEAR! WELCOME & PLENARY KENOTE One-dAY SYMPOSIUM APRIl 16-18, 2013 Chemistry HILTON SaN DIEGO RESORT & SPA · SaN DIEGO, CA SHORT COURSES April 16-17 OPTIMIZING SMALL MOLECULES Anti-INFLAMMATORIES FOR TOMORROW’s THERAPEUTICS FRagMENT-BASED DRUG DISCOVERY CONSTRAINED PEPTIDES AND APRIL 16-17 MacROCYCLICS DRUG DISCOVERY NOBEL LauREATE 4TH ANNUAL April 17-18 PLENARY KEYNOTE April 16 • 4:30pm Anti-Inflammatories KINASE INHIBITOR CHEMISTRY Jack W. Szostak, Ph.D., Investigator, Howard Hughes Medical Institute; Professor of Genetics, Harvard Medical School 8TH ANNUAL PROtein-PROTEIN INTERacTIONS Fragment-Based Drug Discovery SYMPOSIUM GPCR-BASED DRUG DESIGN Property-Based Drug Design INAUGURAL Improving the Drug Discovery Process by Optimizing Bio-Physical Properties Constrained Peptides and SPONSOR & EXHIBIT OPPORTUNITIES Macrocyclics Drug Discovery EVENT FEATURES More than 100 Technical Presentations APRIL 17-18 HOTEL & TRAVEL INFORMATION 10 Short Courses Exclusive Exhibit & Poster Viewing Hours Interactive Roundtable, Breakout & Panel Discussions 4TH ANNUAL REGISTRATION INFORMATION 30+ Scientific Posters 400 High-Level Participants Kinase Inhibitor Chemistry Dedicated Networking Opportunities REGISTER AND HELP MAKE THIS OUR BEST YEAR EVER! 6TH ANNUAL CLICK HERE TO Protein-Protein Interactions REGISTER ONLINE! INAUGURAL Organized by GPCR-Based Drug Design Cambridge Healthtech Institute HILTON SaN
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Drug Design Project: Information and Tips Part of Your Grade in Drug
Drug Design Project: Information and Tips Part of your grade in Drug Design courses will come from a development of a “Drug Design Project”. Students who will take the second quarter of Drug Design in Winter 2010 will be able to take their project to a completion and will present their projects during the annual Drug Design Poster Event. Students taking Chem 162A during the Fall 2009 will be able accomplish three important milestones toward completing the Project. Part of your grade will be based on your success in completing these milestones. The milestones that you are expected to complete during the Chem162A are: 1) Select and characterize a disease that you plan to cure with a drug that you could design 2) Identify and provide validation for a molecular target relevant to the disease you selected. Note that you are allowed to embark into “chemical genomics” pathway in case there are no obvious targets. The latter approach still requires a thorough understanding of the disease and if you go with “chemical genomics” route, you should provide a comprehensive description of the biology and biochemistry of the disease 3) Propose an approach to identify lead compounds for development of a drug for the disease you selected. If you have a validated target, this typically means development of a specific biochemical, cell-based, or in silico assay. If you follow “chemical genomic” route, this would mean development of high-throughput cell-based screening methods. A more detailed guidance on how to meet these milestones is given on next pages. You are expected to turn in your work on each milestone by the due dates specified; you’ll automatically receive 3 points per each milestone if you submit a significantly developed work by the due date.
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Computer Aided Drug Design: a Novel Loom to Drug Discovery
Organic and Medicinal Chemistry International Journal ISSN 2474-7610 Review Article Organic & Medicinal Chem IJ Volume 1 Issue 4 - February 2017 Copyright © All rights are reserved by Syed Sarim Imam DOI: 10.19080/OMCIJ.2017.01.555567 Computer Aided Drug Design: A Novel Loom To Drug Discovery Syed Sarim Imam and Sadaf Jamal Gilani* Department of Pharmaceutics, Glocal School of Pharmacy, The Glocal University, Saharanpur, U.P, India Department of Pharmaceutical Chemistry, Glocal School of Pharmacy, The Glocal University, Saharanpur, U.P, India Submission: February 15, 2017; Published: February 20, 2017 *Corresponding author: Dr. Sadaf Jamal Gilani, Associate Professor, Department of Pharmaceutical Chemistry, Glocal School of Pharmacy, The Glocal University, Saharanpur-247121, Uttar Pradesh, India, Tel: ; Email: Abstract Computer-Aided Drug Design (CADD) is a growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery design, development, and optimization. This technique of drug discovery and development are very time and resources consuming processes. But this tool can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. This application has been widely used in the biomedical arena, in silico design is being utilized Here in this mini review, we present overviews of computational methods used in the different arena of drug discovery and focusing some of theto expedite recent successes. and facilitate hit identification, hit-to-lead selection, optimize the pharmacokinetic profile, toxicity profile and avoid safety issues. Keywords: Computer-aided drug design (CADD); In-silico design; Computational methods Introduction b.
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Structural Biology and Drug Design
Structural biology and drug design Bjørn Dalhus Research Scientist, Department for Medical Biochemistry & Microbiology, Oslo University Hospital, Rikshospitalet E-mail: [email protected] Telephone: 23070908 Group: Magnar Bjørås (http://www.rr-research.no/bjoras) Main research area: DNA repair & Structural Biology Dalhus - MBV-INF4410/9410 November 2012 Outline of lecture Part 1 1. Group presentation 1. DNA repair & methods 2. Core facility 2. Drug design 1. From idea to market 2. Strategies 3. Target selection and validation 3. Role of structural biology in drug design 1. 3D structure determination (X-ray and NMR) 2. Experimental challenges 3. 3D models – strengths and weaknesses (demo) Part 2 4. Structure-based drug design 1. Ligand based methods 2. Receptor-based methods 5. Docking 1. Ligands; databases, ligand preparation 2. Target preparation 3. Running a docking job 4. Scoring and validation Dalhus - MBV-INF4410/9410 November 2012 Outline of lecture Part 1 1. Group presentation 1. DNA repair & methods 2. Core facility 2. Drug design 1. From idea to market 2. Strategies 3. Target selection and validation 3. Role of structural biology in drug design 1. 3D structure determination (X-ray and NMR) 2. Experimental challenges 3. 3D models – strengths and weaknesses (demo) Part 2 4. Structure-based drug design 1. Ligand based methods 2. Receptor-based methods 5. Docking 1. Ligands; databases, ligand preparation 2. Target preparation 3. Running a docking job 4. Scoring and validation Dalhus - MBV-INF4410/9410 November 2012 DNA damage • Since DNA carries all genetic information, one would think that DNA is a chemically stable molecule • However, there are betweeen 10.000 – 100.000 damages per genom per cell per day in humans • Ideally, all these damages must be detected and/or removed/repaired Hoeijmakers, J.
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Recent Advances in Developing Small Molecules Targeting Nucleic Acid
International Journal of Molecular Sciences Review Recent Advances in Developing Small Molecules Targeting Nucleic Acid Maolin Wang 1,2,3, Yuanyuan Yu 1,2,3, Chao Liang 1,2,3, Aiping Lu 1,2,3,* and Ge Zhang 1,2,3,* 1 Institute of Integrated Bioinfomedicine and Translational Science (IBTS), School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China; [email protected] (M.W.); [email protected] (Y.Y.); [email protected] (C.L.) 2 Shenzhen Lab of Combinatorial Compounds and Targeted Drug Delivery, HKBU Institute of Research and Continuing Education, Shenzhen 518000, China 3 Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong 999077, China * Correspondence: [email protected] (A.L.); [email protected] (G.Z.); Tel.: +852-3411-2456 (A.L.); +852-3411-2958 (G.Z.) Academic Editor: Mateus Webba da Silva Received: 2 March 2016; Accepted: 9 May 2016; Published: 30 May 2016 Abstract: Nucleic acids participate in a large number of biological processes. However, current approaches for small molecules targeting protein are incompatible with nucleic acids. On the other hand, the lack of crystallization of nucleic acid is the limiting factor for nucleic acid drug design. Because of the improvements in crystallization in recent years, a great many structures of nucleic acids have been reported, providing basic information for nucleic acid drug discovery. This review focuses on the discovery and development of small molecules targeting nucleic acids. Keywords: nucleic acids; nucleic acids targeting; small molecules; DNA drug discovery; RNA drug discovery 1.
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Polypeptide and Protein Modeling for Drug Design Encyclopedia Of
Dr. Megan L. O’Mara and Dr. Evelyne Deplazes Final Author Version Polypeptide and Protein Modeling for Drug Design Encyclopedia of Computational Neuroscience Synonyms Computational modeling, structure-aided drug design, computational structural biology Definition Modeling of polypeptides and proteins, often referred to as biomolecular or molecular modeling, encompasses the use of theoretical models and computational methods to model the structure and dynamics of molecules of biological interest such as peptides, proteins and small organic molecules (ligands). The aims of protein and peptide modeling for drug design include: (i) modeling the three- dimensional structure of proteins of current or potential drug targets; (ii) identifying and characterizing the structural dynamics associated with the function of a particular protein or peptide; and (iii) predicting the structure and molecular interactions of protein-ligand complexes. This knowledge underpins structure-based and rational drug design. It can aid in the design and optimization of drug molecules by shedding light on their mode of action, specificity and selectivity. Detailed Description Background The number of protein structures solved experimentally and deposited in the Protein Data Bank (PDB) has risen exponentially since its inception the 1970’s (RSCB Protein Data Bank). This has been accompanied by a rapid growth in computational power and continuous improvements in modeling algorithms, greatly enhancing the complexity, size and diversity of the proteins that can be simulated. Together, the experimental and computational breakthroughs initiated in the 1970’s have contributed enormously to our knowledge of protein structure and structural conservation within protein superfamilies. In 2013, the contribution of computational modeling to our understanding of protein structure and biochemical function was recognized by the award of the Nobel Prize in Chemistry to three of the founders of the field, Martin Karplus, Michael Levitt and Arieh Warshel (The Nobel Prize in Chemistry 2013 - Press Release 2013).
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Rise of Virtual Drug Design Tools
MOJ Bioorganic & Organic Chemistry Editorial Open Access Rise of virtual drug design tools Editorial Volume 1 Issue 2 - 2017 The invention of successful drug candidates or lead molecules Michael Rajesh Stephen always requires a brainstorming effort, laborious time and as well Department of Chemistry and Biochemistry, University of as a fortune. Hence asserting the drug-likeness properties of the lead Wisconsin-Milwaukee, USA molecule(s) at an early stage of the invention will efficiently propel the drug discovery. Nowadays there are a lot of in silico tools available Correspondence: Michael Rajesh Stephen, Department (open access as well as copyrighted software) which can almost of Chemistry and Biochemistry, University of Wisconsin- Milwaukee, 3210 North Cramer Street, Milwaukee, Wisconsin predict whether the proposed molecule(s) is going to be successful in 53211, USA, Tel +1 414–712–4074, Email [email protected] further studies. There are various properties taken into account while screening through silico drug designing tools. For, e.g., solubility, Received: July 27, 2017 | Published: July 28, 2017 topological polar surface area (TPSA), pKa, LogP, hydrogen bond donor/acceptor, molecular size, binding site, blood brain barrier penetration (for CNS drugs), rule of 5.1 Lipinski rule of five was the prime influential parameter while is also equally important to achieve the same and desired result designing the drugs. More than a decade there was parallel research in obtained from the virtual screening. the field of Fragment Based Drug Discovery (FBDD)2 was conducted. In the FBDD, two small chemical leads serve as building blocks for Acknowledgements the outcome of a desired biological effect.
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