Quantitative Molecular Pharmacology and Informatics in Drug Discovery

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The chapters discuss new methods to study target identification, genome analysis, cheminformatics, protein analysis, and text mining. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials, software workflows, reagents and on-line resources, together with step-by-step, readily reproducible laboratory and computational protocols, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and thorough, Bioinformatics and Drug Discovery, Third Edition is a valuable resource for anyone interested in drug design, including academicians biologists, informaticists and data scientists, chemists, and biochemists , clinicians, and pharmaceutical scientists. JavaScript is currently disabled, this site works much better if you enable JavaScript in your browser.

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Methods in Molecular Biology Free Preview. Includes cutting-edge methods and protocols Provides step-by-step detail essential for reproducible results Contains key notes and implementation advice from the experts see more benefits. While toxicity was traditionally assessed using animal models during the late stage of drug development, predictive toxicology required the development of in silico and in vitro methods executable at large scale and sooner in the drug discovery workflow.

In silico methods essentially involve quantitative structure activity relationships QSAR where potentially toxic compounds are filtered out from libraries based on their structure. In vitro assays rely on specialised cell lines eg hepatocytes for liver toxicity , microscale physiological systems eg organ-on-a-chip and small animal models eg zebrafish, C. To further decrease the compound attrition rate due to lack of efficacy, scientists moved from a target-based approach to a phenotypic strategy where assays take advantage of more biologically-relevant models, including mono- or co-cultured cells, organoids and small animal models such as those described previously.

HTS campaigns are also performed using smaller and more focused libraries composed of a few hundred thousand compounds pre-assembled using QSAR. Chemistry efforts to expand the actual druggable chemical space are under way to better address biological target structural diversity. A short-term tactic used by most major pharmaceutical companies was to acquire new drug pipeline content through merger and acquisition activities.

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Longer-term, several companies opted to decentralise their research programmes via collaborative efforts with external partners. Several academic-industry collaborations were then initiated during the last decade, allowing partners to share expertise and knowledge on disease mechanisms, novel drug targets and assay technologies.

While academic collaborators can profit from industrial know-how in development and specialised resources including medicinal chemistry, HTS and preclinical study setups, industrial partners can access new discoveries and ideas from academia to enhance their innovation potential. Additionally, translational medicine contributes to improve the drug discovery paradigm. With the aim of bringing new drugs to the market more rapidly and safely, translational medicine relies on a workflow involving multiple feedbacks from clinicians, including pathologists, at the different stages of drug development Figure 2.

Quantitative pathology, otherwise known as digital pathology, emerged as a new discipline playing a pivotal role in translational medicine-based drug discovery. In the early times of modern drug discovery, the main technological arsenal available to scientists was comprised of microscopes, pipettes, test tubes and primitive immunoassays, such as radioimmunoassays RIAs.

With the emergence of industrialised target-based drug discovery, microscopes were relegated to a second-string role while pipettes, test tubes and immunoassays underwent significant improvements leading to automated liquid handlers, microplates, ultrasensitive non-radiometric immunoassays and associated multilabel detectors.

Ironically, scientists were replaced by robots during large-scale assay execution HTS. Novel microscopy-based technologies such as high-content screening HCS and high-content image analysis HCIA have proven to be very valuable in the most recent drug discovery efforts. Not only does generating high quality images allow for detection of a specific target signal, it enables recording of holistic phenotypic changes happening in the whole cell, organoid or small organism analysed as well.

However, literature shows that most of the high content data published so far only relied on a few image-based features measured from all samples tested, limiting access to more complete valuable phenotypic information available 8. The lack of advanced technologies allowing for the multiparametric analysis of all collectable data was originally hypothesised as being one of the major impediments limiting the potential of HCS and HCIA. Artificial intelligence including machine learning is used to alleviate these limitations.

Supervised machine learning SML software helps to perform automatic phenotypic classification. Such software is considered essential for high content data analysis — even if it comes at a cost — as expert pre-identified references are required to set biologically-relevant predictive models. To overcome this bottleneck, development of enabling methods to decipher high-content screening results, unbiased from existing control phenotypes, is currently under way.

These methods are based on modelling data issued from unsupervised multiparametric analysis that create self-organising maps SOMs , which eventually helps grouping treatments that generate similar phenotypical responses 9. That approach, referred to as active learning, has the potential to identify novel chemotypes and cellular phenotypes while confirming expected hits on already identified targets. Results obtained to date show that active learning significantly reduces the time and costs to reveal the same phenotypic targets identified using SML HCS and HCIA are both based on classical optical microscopy with a resolution limited by diffraction to approximately nm.

Stimulated emission depletion STED microscopy technique, whose inventors received the Nobel prize in chemistry, overcomes the diffraction-limited resolution barrier by using a pair of lasers to control the excitation state of fluorescent molecules in a targeted manner allowing resolution of 50nm or less 1. STED is primarily a point-scanning technique where the fluorescence spot produced by a first laser is sharpened by stimulated emission induced by the second laser.

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STED provides much sharper images compared to classical microscopy allowing visualisation of individually-labelled biomolecules even in a complex environment. For instance, nanoscale STED imaging of green fluorescent protein-labelled neurons was demonstrated in living brain slices Improvements in cell imaging technologies discussed above are complemented by the development of enhanced cellular models mirroring in vivo systems. For instance, in vitro cell-based assays help in reproducing the complexity of biological environment and are generally predictive of how a compound behaves in vivo.

Immortalised cell lines, primary cell cultures and, more recently, human pluripotent stem cells hPSCs have shown to be invaluable phenotypic models for basic research and drug discovery efforts. The fact that they proliferate indefinitely and are easy to maintain in culture at low cost makes them very convenient, namely for executing HTS campaigns.

Free molecular visualization program for displaying macromolecules, building molecules, multiple sequence alignments.

Open source GPL , interactive, high quality molecular visualization system. QuteMol visualization techniques are aimed at improving clarity and an easier understanding of the 3D shape and structure of large molecules or complex proteins. Free molecular explorer for protein structure visualization, validation and analysis.

Mainained by Dr. Nymeyer's Group, Inst. Program for the macromolecular structure visualization CueMol was formerly called "Que". CueMol aims to visualize the crystallographic models of macromolecules with the user-friendly interfaces. Molecular visualization and computation package.

Bioinformatics and Drug Discovery

Free and open source software. Visualization tool and graphical user interface of the Chil 2 suite, with analysis tools, database integration and ruby interface. Open for general research. Visualization application and molecular modeling toolkit Molecular mechanics and dynamics, structure-based screening. Free for non-profit academic uses. Standalone molecular modeling and visualization application. Provides a framework for developing molecular visualization functionality.

Can be used as the visualizaion component of BALL. Free and opensource. Program for molecular graphics visualisation. Free open source molecular visualization software adapted from the program RasMol. RasTop wraps a user-friendly graphical interface around the "RasMol molecular engine". Developed for educational purposes and for the analysis of macromolecules at the bench.

Visualization tool for biomolecular structures, sequences, and sequence alignments. Maintained and distributed by the NCBI. Free, modular, multi-platform software package for biomolecular visualization and modeling. Bodil aims to provide easy three-dimensional molecular graphics closely integrated with sequence viewing and sequence alignment editing.

Free software for presentation of molecules. BARISTA visualization functions create, display, and manipulate 3D depictions of molecular structures based on results computed by molecular computation programs such as Conflex, and are designed specifically to facilitate the analysis of these results. Visualization tool for biomolecular structures and small molecules. Provided by Molsoft. PyMOL on the iPad.

Quantitative Molecular Pharmacology and Informatics in Drug Discovery

High-performance 3D molecular visualizer, designed from the ground up for the iPad. The app enables the general public, researchers and scholars to search the Protein Data Bank and visualize protein structures using either a WiFi or cellular data connection. High-quality molecular visualization app for the iPad, iPhone and iPod Touch. Provided by MolySym. CueMol for iOS.

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Interactive macromolecular viewer for structural biologists. Application designed to enable students and professionals to build, construct, modify and examine molecules in 3D. Chem3D for iPad. Chem3D for iPad enables scientists to view and manipulate 3D images of chemical and biochemical structures. Re-imagined for the iPad, the Chem3D app features a facile user interface to manipulate images using common touch, pinch and swipe gestures. Interactive 3D molecular viewer designed specifically for the iPad, iPhone and iPod touch.

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  8. CMol allows the user to open and view PDB files with complete control over the representations and colours used for individual chains, residues and atoms.