Virtual Screening for Anti-HIV-1 RT and Anti-HIV-1 PR Inhibitors from the Thai Medicinal Plants Database: A Combined Docking with Neural Networks Approach

Sangma, Chak; Chuakheaw, Daungmanee; Jongkon, Nipa; Saenbandit, Kittipong; Nunrium, Peerapol; Uthayopas, Putchong; Hannongbua, Supa

doi:10.2174/1386207054546469

Cited by 20 publications

(13 citation statements)

References 31 publications

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“…Several recent examples include applications of probabilistic Naïve Bayes methods to improve scoring functions for docking [108], applications of NNs to virtual screening in combination with docking methods [109], and combination of kNN and docking methods to achieve improved results in QSAR [110]. In general, one may observe a growing tendency to combine different techniques and use consensus-based methods, such as the lateral validation [111].…”

Section: Docking and Ai Methodsmentioning

confidence: 99%

Artificial Intelligence Approaches for Rational Drug Design and Discovery

Duch¹,

Swaminathan²,

Meller

2007

CPD

177

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Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.

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Section: Docking and Ai Methodsmentioning

confidence: 99%

Artificial Intelligence Approaches for Rational Drug Design and Discovery

Duch¹,

Swaminathan²,

Meller

2007

CPD

177

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“…The ranking of HIV protease inhibitors after high throughput docking has been improved by training with Bayesian classifiers [72]. Docking has been combined with selforganizing neural networks to screen a database of Thai medicinal plants [73].…”

Section: Docking Methodsmentioning

confidence: 99%

“…This mapping is based on the inherent topological structure hidden in the vectors. Docking has been combined with self-organizing neural networks to screen a database of Thai medicinal plants for inhibitors of HIV protease [73].…”

Section: Data Mining and Fuzzy Logic Approachesmentioning

confidence: 99%

Structure Based Drug Design for HIV Protease: From Molecular Modeling to Cheminformatics

Volarath

Harrison²,

Weber³

2007

CTMC

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Significant progress over the past decade in virtual representations of molecules and their physicochemical properties has produced new drugs from virtual screening of the structures of single protein molecules by conventional modeling methods. The development of clinical antiviral drugs from structural data for HIV protease has been a major success in structure based drug design. Techniques for virtual screening involve the ranking of the affinity of potential ligands for the target site on a protein. Two main alternatives have been developed: modeling of the target protein with a series of related ligand molecules, and docking molecules from a database to the target protein site. The computational speed and prediction accuracy will depend on the representation of the molecular structure and chemistry, the search or simulation algorithm, and the scoring function to rank the ligands. Moreover, the general challenges in modern computational drug design arise from the profusion of data, including whole genomes of DNA, protein structures, chemical libraries, affinity and pharmacological data. Therefore, software tools are being developed to manage and integrate diverse data, and extract and visualize meaningful relationships. Current areas of research include the development of searchable chemical databases, which requires new algorithms to represent molecules and search for structurally or chemically similar molecules, and the incorporation of machine learning techniques for data mining to improve the accuracy of predictions. Examples will be presented for the virtual screening of drugs that target HIV protease.

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“…Surprisingly, even without any success, the method of extracting natural products from plants, in an attempt to find unidentified compounds for drug activity testing still continues today as a standard procedure in Thailand. Only recently, in silico screening and multidisciplinary approaches in drug discovery were introduced and performed in Thailand [12][13][14][15][16].…”

Section: Computer-aided Drug Discovery From Thai Traditional Knowledgementioning

confidence: 99%

Computer Techniques for Drug Development from Thai Traditional Medicine

Sangma

Chuakheaw²,

Jonkon³

et al. 2010

CPD

Self Cite

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Thailand has a vast number of plant species. Up to 3000 of them are believed by traditional Thai medicine to possess some biological activity with which researchers have attempted for many years to identify and formulate new drugs. Many chemical compounds from Thai plant species are identified and tested for biological activity that may enable them to be declared lead compounds in drug discovery. Modern methods of drug discovery are rarely used to rationalize and speed-up the process. Within this decade, the first structural database of Thai medicinal plants, Chemiebase, has been built as a platform for virtual screening, using knowledge from Thai traditional medicine. Although this effort is a promising protocol which can be used to validate Thai traditional medicine, there exists another problem that should be resolved before proceeding: It is almost impossible to trace the knowledge to its primary source. Thai traditional knowledge has been passed on orally or - less frequently - in ancient texts. We have built another database, the Thai Herbal Repository Access Initiative (THRAI) database, in order to compile the traditional knowledge into electronic format suitable for the drug design process. Three examples using data from these databases and other computer-aided drug discovery methods to rationalize Thai traditional medicine are presented here, starting with virtual screening exercised on anti-HIV-1 reverse transcriptase, anti-HIV-1 protease, anti-influenza A neuraminidase, and anti-cyclooxygenase (COX), candidates. The second example consists of the use of molecular modeling to propose drug mechanism for anti-tumor compounds. The last one is the study on toxicity assessment of some compounds from Thai medicinal plants.

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Virtual Screening for Anti-HIV-1 RT and Anti-HIV-1 PR Inhibitors from the Thai Medicinal Plants Database: A Combined Docking with Neural Networks Approach

Cited by 20 publications

References 31 publications

Artificial Intelligence Approaches for Rational Drug Design and Discovery

Artificial Intelligence Approaches for Rational Drug Design and Discovery

Structure Based Drug Design for HIV Protease: From Molecular Modeling to Cheminformatics

Computer Techniques for Drug Development from Thai Traditional Medicine

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