Analogue-based Drug Discovery Research Articles

Neural Network Methodology for the Identification and Classification of Lipopeptides Based on SMILES Annotation

Artificial Neural Networks can be applied for the identification and classification of prospective drug candidates such as complex compounds, including lipopeptide, based on their SMILES string representation. The training of neural networks is done with SMILES strings, which are predictive of structural identification; the ANNs are efficient of correctly classifying all compounds, substructures and their analogues distinguishing the drugs based upon atomic organization to obtain lead optimization in drug discovery. The proficiency of the trained ANN models in recognizing and classifying the analogous compounds was tested for analysis of similar compounds, which were not taken previously for training and achieved results with correct classification in the validation set. The best result was achieved with 10 numbers of hidden layers. The R2 value for training is 0.90586; the R2 value for testing is 0.99508; the R2 value after validation is 0.94151; the final value of R2 for total sets is 0.89456. The graphs are plotted between 21 epochs and mean square error (MSE) to report the performance of the model. The value of 798.1735 for the gradient of the curve after 21 iterations and 6 validation checks was obtained. A successful model was developed for the identification and classification of lipopeptides from their SMILES annotation that efficiently classifies similar compounds and supports in decision making for analogue-based drug discovery. This will help in appropriate lead optimization studies for the prediction of potential anticancer and antimicrobial lipopeptide-based therapeutics.

Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery

Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.

An Approach to Computer Aided Drug Design of some Bioactive Cinnamoyl Hydrazones, In Silico and Docking Studies as Possible COX-2 Selective Inhibitors

Recently structural analogue-based drug discovery has become an important tool for designing more potent drugs. This study uses SAR, pharmacophore study and structural analogue-based novel drugs to design selective inhibitor molecules using internet-based tools. Substituted acyl hydrazones are known for wide variety of biological activities, such as analgesic, anti–inflammatory, anti-microbial, anti-convulsant, anti-platelet, anti-tubercular, antiviral, schistomiasis and anti-tumoral activities. In the last decade several cinnamic acid derivatives were reported as potent lipoxygenase inhibitors, antioxidants and anti-inflammatory agents. We found it interesting to combine these particular molecules and design cinnamoyl hydrazones, a series of N-[(1E)-2-substituted phenyl-1-{N'-[(1E)-phenyl methylidene] hydrazine carbonyl} eth-1-en-1-yl] benzamides. The designed compounds were predicted for their drug likeness and oral bioavailability. Selected compounds were docked with COX-2 enzyme and found that they were showing similar interactions as that of SC-558, 1000 time selective standard COX-2 inhibitor.

Analogue-based Drug Discovery III

Analogue-based Drug Discovery III

Editorial: Lucky Number 8!

Editorial: Lucky Number 8!

Analogue-based drug discovery: Contributions to medicinal chemistry principles and drug design strategies. Microtubule stabilizers as a case in point (Special Topic Article)

The benefits of utilizing marketed drugs as starting points to discover new therapeutic agents have been well documented within the IUPAC series of books that bear the title Analogue-based Drug Discovery (ABDD). Not as clearly demonstrated, however, is that ABDD also contributes to the elaboration of new basic principles and alternative drug design strategies that are useful to the field of medicinal chemistry in general. After reviewing the ABDD programs that have evolved around the area of microtubule-stabilizing chemo-therapeutic agents, the present article delineates the associated research activities that additionally contributed to general strategies that can be useful for prodrug design, identifying pharmacophores, circumventing multidrug resistance (MDR), and achieving targeted drug distribution.

Analogue-Based Drug Discovery: Contributions to Medicinal Chemistry Principles and Drug Design Strategies. Microtubule Stabilizers as a Case in Point (special topic article)

Analogue-Based Drug Discovery: Contributions to Medicinal Chemistry Principles and Drug Design Strategies. Microtubule Stabilizers as a Case in Point (special topic article)

Analogue-based Drug Discovery

Analogue-based drug discovery , Analogue-based drug discovery , کتابخانه مرکزی دانشگاه علوم پزشکی تهران

Analogue-based Drug Discovery II

Analogue-based Drug Discovery II