From Virtual Screening to Bioactive Compounds by Visualizing and Clustering of Chemical Space

2011 ◽  
Vol 31 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Alexander Klenner ◽  
Volker Hähnke ◽  
Tim Geppert ◽  
Petra Schneider ◽  
Heiko Zettl ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Bioactive Compounds ◽  
Chemical Space

scholarly journals Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

2021 ◽  
Vol 9 ◽  
Author(s):  
Kauê Santana ◽  
Lidiane Diniz do Nascimento ◽  
Anderson Lima e Lima ◽  
Vinícius Damasceno ◽  
Claudio Nahum ◽  
...  
Keyword(s):  
Natural Products ◽  
Virtual Screening ◽  
Bioactive Compounds ◽  
Chemical Space ◽  
Structural Diversity ◽  
Industrial Applications ◽  
Bioactive Molecules ◽  
Natural Sources ◽  
Compound Libraries ◽  
Pharmacokinetic Properties

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Unraveling the action mechanism of Buyang Huanwu Tang (BYHWT) for Cerebral Ischemia by Systematic Pharmacological Methodology

2020 ◽  
Vol 23 ◽  
Author(s):  
Shi-tang Ma ◽  
Ning Zhang ◽  
Ge Hong ◽  
Cheng-tao Feng ◽  
Sheng-wei Hong ◽  
...  
Keyword(s):  
Cerebral Ischemia ◽  
Virtual Screening ◽  
Signaling Pathways ◽  
Bioactive Compounds ◽  
Enrichment Analysis ◽  
Material Base ◽  
Lead Discovery ◽  
Binding Effect ◽  
Bioactive Ingredients ◽  
Compound Target

Background: Buyang Huanwu Tang (BYHWT) and relevant Traditional Chinese medicine (TCM) has its unique advantages in the treatment of cerebral ischemia. However, its pharmacological mechanism have not been fully explained. Objective: Base on the multi-component, also the entire disease network targets, the present study set out to identify major bioactive ingredients, key disease targets, and pathways of BYHWT against cerebral ischemia disease by systematic pharmacological methodology. Methods: Both the bioactive compounds from the BYHWT and the positive drugs against cerebral ischemia were fully investigated. The binding targets of the positive drugs were then obtained. A virtual screening protocol was then used to highlight the compound-target interaction. And network was constructed to visual the compound-target binding effect after docking analysis. Moreover，the targets enrichment analysis for biological processes and pathways were revealed to further explore the function of bio-targets protein gene and its role in the signal pathway. Results: A total of 382 active ingredients of the BYHWT and 23 candidate disease targets were identified. Virtual screening results indicated that multiple bioactive compounds targeted multiple proteins. Each compounds act on one or more targets. The mechanisms were linked to 20 signaling pathways, and the key mechanism was related to serotonergic synapse, calcium signaling pathway and camp signaling pathways. Conclusion: The present study explored the bioactive ingredients and mechanisms of BYHWT against cerebral ischemia by systematic pharmacological methodology. the novel methodology would provide a reference for the lead discovery of precursors, disease mechanism and material base for TCM.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

scholarly journals Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES

2021 ◽  
Author(s):  
AkshatKumar Nigam ◽  
Robert Pollice ◽  
Mario Krenn ◽  
Gabriel dos Passos Gomes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Chemical Space ◽  
Generative Models ◽  
Inverse Design ◽  
Learning Models ◽  
Structure Modification ◽  
Design Models ◽  
Comparable Performance ◽  
And Training

Inverse design allows the design of molecules with desirable properties using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED – a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. We achieve comparable performance on typical benchmarks without any training. We demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. We anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wide adoption.

Inverse Virtual Screening of Antitumor Targets: Pilot Study on a Small Database of Natural Bioactive Compounds

2011 ◽  
Vol 74 (6) ◽  
pp. 1401-1407 ◽  
Author(s):  
Gianluigi Lauro ◽  
Adriana Romano ◽  
Raffaele Riccio ◽  
Giuseppe Bifulco
Keyword(s):  
Pilot Study ◽  
Virtual Screening ◽  
Bioactive Compounds

Pharmacophore based virtual screening for identification of marine bioactive compounds as inhibitors against macrophage infectivity potentiator (Mip) protein of Chlamydia trachomatis

RSC Advances ◽  
2016 ◽  
Vol 6 (23) ◽  
pp. 18946-18957 ◽  
Author(s):  
Vijayan Ramachandran ◽  
Elavarasi Padmanaban ◽  
Kalaiarasan Ponnusamy ◽  
Subbarao Naidu ◽  
Manoharan Natesan
Keyword(s):  
Chlamydia Trachomatis ◽  
Virtual Screening ◽  
Sexually Transmitted Diseases ◽  
Bioactive Compounds ◽  
Virulence Factor ◽  
Sexually Transmitted ◽  
Macrophage Infectivity Potentiator ◽  
Macrophage Infectivity

Macrophage infectivity potentiator (Mip) is the virulence factor fromChlamydia trachomatisthat is primarily responsible for causing sexually transmitted diseases (STDs) and blindness.

scholarly journals Expanding the fragrance chemical space for virtual screening

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Lars Ruddigkeit ◽  
Mahendra Awale ◽  
Jean-Louis Reymond
Keyword(s):  
Virtual Screening ◽  
Chemical Space

scholarly journals Automated Framework for Developing Predictive Machine Learning Models for Data-Driven Drug Discovery

2020 ◽  
Author(s):  
Bruno J. Neves ◽  
José T. Moreira-Filho ◽  
Arthur C. Silva ◽  
Joyce V. V. B. Borba ◽  
Melina Mottin ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Best Practices ◽  
Chemical Space ◽  
Biological Data ◽  
Data Driven ◽  
Learning Models ◽  
Structure Activity ◽  
Research Problems ◽  
Qsar Models ◽  
Machine Learning Models

In this manuscript we describe the development of an automated framework for the curation of chemogenomics data and to develop QSAR models for virtual screening using the open-source KNIME software. The workflow includes four modules: (i) dataset preparation and curation; (ii) chemical space analysis and structure-activity relationships (SAR) rules; (iii) modeling; and (iv) virtual screening (VS). As case studies, we applied these workflows to four datasets associated with different endpoints. The implemented protocol can efficiently curate chemical and biological data in public databases and generates robust QSAR models. We provide scientists a simple and guided cheminformatics workbench following the best practices widely accepted by the community, in which scientists can adapt to solve their research problems. The workflows are freely available for download in GitHub.

scholarly journals Drug discovery by integration of pharmacophore modeling, virtual screening and biological evaluation by means of bioinformatics resources

2016 ◽  
Author(s):  
Serena Dotolo ◽  
Angelo Facchiano
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Development Process ◽  
Chemical Space ◽  
Pharmacophore Modeling ◽  
Biological Evaluation ◽  
Lead Compounds ◽  
Computational Screening ◽  
Computational Strategy

Drug discovery is a step-by-step process very important in biopharmaceutical field. We are interested in identifying new investigational drug-likes as potential inhibitors of determinate biological-therapeutic targets, trying to decrease the side effects and to safeguard the human health. However, it is a long and very expensive process. Therefore, we are using a new computational strategy, based on Pharmacophore modeling, to select bioactive substances (natural or synthetic), through the integration of bioinformatics online tools and local resource and platforms, in order to include into the strategy also knowledge from high-throughput studies, for new potential lead compounds generation-optimization, trying to accelerate the early phase of the drug development process. The protocol of this new computational strategy is characterized by a multi-step design focused on: 1) screening in RCSB-PDB for a crystal structure of a specific biological target, suitable for the following steps; 2) pharmacophore modeling and virtual computational screening, by using public domain databases of bioactive compounds, as the ZINC12 database [5], in order to find a promising molecule that could become a new potential medicine. 3) molecular and biological evaluation, to check the compounds selected by virtual screening, for their biological properties through public databases, as PubChem Compound, SciFinder, and Chemicalize to trace their origin and underline their most important physical-chemical features, PathPred (an enzyme-catalyzed metabolic pathway predictor server) to highlight and identify their biosynthetic-metabolic pathways and investigating the biotransformation of best candidates, analyzing their metabolites and their potential biological activity. Moreover, ADMET/toxicity predictor server applying the Lipinski-Veber filter are used to calculate the bioavailability the ADMET/toxicity properties. After this check, only molecules with good bioavailability, good predicted activity and good ADMET properties are considered as hits compounds or drug-likes to direct the design of next experimental assays [6]. Finally, the lead compounds selected are analyzed through molecular dynamics simulations. 4) simulations of molecular dynamics on the best lead compounds, to investigate atomic details of protein-compound molecular interactions in different conditions (different organic solutions, organisms and systems). REFERENCES [1] Dubey A, Facchiano A, Ramteke PW, Marabotti A. “In silico approach to find chymase inhibitors among biogenic compounds.” Future Med Chem. 2016; 8(8):841-51 [2] Dubey A, Marabotti A, Ramteke PW, Facchiano A. "Interaction of human chymase with ginkgolides, terpene trilactones of Ginkgo biloba investigated by molecular docking simulations.” Biochem Biophys Res Commun. 2016; 473(2):449-54. [3] Katara P. “Role of bioinformatics and pharmacogenomics in drug discovery and development process”. Netw Model Anal Health Inform Bioinforma 2013; 2: 225-230. [4] Sunseri J. and Koes D. R. “Pharmit: Interactive Exploration of Chemical Space”.Nucl. Acids Res. 2016; 44(W1): W442-448. [5] Irwin J.J. and Shoichet B.K. “ZINC- A free database of Commercially Available Compounds for Virtual Screening”. J.Chem.Inf.Model. 2005; 45: 177-182. [6] Kaserer T., Beck K. R., Akram M., Odermatt A., Schuster D. “Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Application Exemplified on Hydroxysteroid Dehydrogenases”.Molecules 2015; 20: 22799–22832.

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