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Pharmaceutical industries are interested in Cysteine-stabilized peptides because they offer an array bioactive properties while being highly stable under a range of physiological conditions. However, it is widely appreciated that only a small fraction of this type of peptides have been experimentally discovered while a large number remain unidentified. However, identification of these cysteine-stabilized...
Machine learning methods are becoming more and more popular in the field of computer-aided drug design. The specific data characteristic, including sparse, binary representation as well as noisy, imbalanced datasets, presents a challenging binary classification problem. Currently, two of the most successful models in such tasks are the Support Vector Machine (SVM) and Random Forest (RF). In this paper,...
QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in drug development through computational chemistry. Similar molecules with just a slight variation in their structure can have quit different biological activity. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR Modeling. Predictions of...
QSAR (quantitative structure-activity relationship) modeling is one of the well developed areas in drug development through computational chemistry. Similar molecules with just a slight variation in their structure can have quit different biological activity. This kind of relationship between molecular structure and change in biological activity is center of focus for QSAR Modeling. Predictions of...
A GPU-accelerated OpenCL implementation of a back-propagation artificial neural network for the creation of QSAR models for drug discovery and virtual high-throughput screening is presented. A QSAR model for HSD achieved an enrichment of 5.9 and area under the curve of 0.83 on an independent data set which signifies sufficient predictive ability for virtual high-throughput screening efforts. The speed-up...
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