Support vector machine (SVM) was applied to predict vasorelaxation effect of different structural molecules. A good classification model had been established, and the accuracy in prediction for the training, test, and overall datasets was 93.0%, 82.6%, and 89.5%, respectively. Furthermore, the model was used to predict the activity of a series of prenylated flavonoids. According to the estimated result, eleven molecules 1-11 were selected and synthesized. Their vasodilatory activities were determined experimentally in rat aorta rings that were pretreated with phenylephrine (PE). Structure-activity relationship (SAR) analysis revealed that flavanone derivatives showed the most potent activities, while flavone and chalcone derivatives exhibited medium activities.

01 Mar 2007·QSAR & Combinatorial Science

Quantitative Structure Vasodilatory Activity Relationship – Synthesis and “In Silico” and “In Vitro” Evaluation of Resveratrol‐Coumarin Hybrids

Author: Alcaide, Carlos ; Vilar, Santiago ; Quezada, Elias ; Orallo, Francisco ; Santana, Lourdes ; Uriarte, Eugenio

AbstractThree theoretical models have been developed for the prediction of vasodilatory activity through a series of 2‐D molecular descriptors. A database of 501 compounds was selected from the literature and, of these compounds, 86 have vasodilatory activity. The QSAR models are capable of differentiating between active and inactive compounds with a level of classification greater than 80%. The models were used to predict the activity of a series of coumarins derived from resveratrol (a natural compound that is present in wine and has good vasodilatory activity) and led to the synthesis of three selected molecules. The sysnthesis of the resveratrol‐coumarin hybrids was efficiently achieved through a straightforward and direct route, and their vasodilatory activities were determined experimentally in rat aorta rings that were precontreated with noradrenaline. The theoretical results (“in silico” evaluation) show very good agreement with the experimental data (“in vitro” evaluation), which provide evidence of the reliability of the theoretical calculations and show their applicability in the rational design of new compounds. The compound predicted by the three models to be active (compound6) was confirmed to be the more active thantrans‐resveratrol.

01 Feb 2006·Journal of Medicinal Chemistry

Probabilistic Neural Network Model for the In Silico Evaluation of Anti-HIV Activity and Mechanism of Action

Article

Author: Santana, Lourdes ; Vilar, Santiago ; Uriarte, Eugenio

A theoretical model has been developed that discriminates between active and nonactive drugs against HIV-1 with four different mechanisms of action for the active drugs. The model was built up using a probabilistic neural network (PNN) algorithm and a database of 2720 compounds. The model showed an overall accuracy of 97.34% in the training series, 85.12% in the selection series, and 84.78% in an external prediction series. The model not only correctly classified a very heterogeneous series of organic compounds but also discriminated between very similar active/nonactive chemicals that belong to the same family of compounds. More specifically, the model recognized 96.02% of nonactive compounds, 94.24% of active compounds that inhibited reverse transcriptase, 97.24% of protease inhibitors, 97.14% of virus uncoating inhibitors, and 90.32% of integrase inhibitors. The results indicate that this approach may represent a powerful tool for modeling large databases in QSAR with applications in medicinal chemistry.

100 Deals associated with Hepronicate

External Link

KEGG	Wiki	ATC	Drug Bank
D01882	-	V03AM	-

R&D Status

10 top approved records.

to view more data

Indication	Country/Location	Organization	Date
Arterial Occlusive Diseases	Japan	Mitsubishi Tanabe Pharma Corp.	01 Jan 1972
Raynaud Disease	Japan	Mitsubishi Tanabe Pharma Corp.	01 Jan 1972
Thromboangiitis Obliterans	Japan	Mitsubishi Tanabe Pharma Corp.	01 Jan 1972
Vascular Diseases	Japan	Mitsubishi Tanabe Pharma Corp.	01 Jan 1972

Clinical Result

Indication

Phase

Evaluation

View All Results

Translational Medicine

Boost your research with our translational medicine data.

view full example data

Deal

Boost your decision using our deal data.

view full example data

Core Patent

Boost your research with our Core Patent data.

view full example data

Clinical Trial

Identify the latest clinical trials across global registries.

view full example data

Approval

Accelerate your research with the latest regulatory approval information.

view full example data

Regulation

Understand key drug designations in just a few clicks with Synapse.

view full example data

AI Agents Built for Biopharma Breakthroughs

Accelerate discovery. Empower decisions. Transform outcomes.

Get started for free today!

Accelerate Strategic R&D decision making with Synapse, PatSnap’s AI-powered Connected Innovation Intelligence Platform Built for Life Sciences Professionals.

Start your data trial now!

Synapse data is also accessible to external entities via APIs or data packages. Empower better decisions with the latest in pharmaceutical intelligence.

Bio

Bio Sequences Search & Analysis

Chemical

Chemical Structures Search & Analysis