International journal of computational biology and drug design
Apr 13, 2015
Machine learning techniques have been widely used in drug discovery and development in the areas of cheminformatics. Aspartyl aminopeptidase (M18AAP) of Plasmodium falciparum is crucial for survival of malaria parasite. We have created predictive mod...
The discovery of peptides possessing high biological activity is very challenging due to the enormous diversity for which only a minority have the desired properties. To lower cost and reduce the time to obtain promising peptides, machine learning ap...
Protein-protein interactions (PPIs) play essential roles in many biological processes. In protein-protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (I...
BACKGROUND: Systems approaches to studying drug-side-effect (drug-SE) associations are emerging as an active research area for both drug target discovery and drug repositioning. However, a comprehensive drug-SE association knowledge base does not exi...
Support vector machines are a popular machine learning method for many classification tasks in biology and chemistry. In addition, the support vector regression (SVR) variant is widely used for numerical property predictions. In chemoinformatics and ...
Expert opinion plays an important role when choosing clusters of chemical compounds for further investigation. Often, the process by which the clusters are assigned to the experts for evaluation, the so-called selection process, and the qualitative r...
Journal of chemical information and modeling
Feb 17, 2015
Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more r...
A large quantity of high throughput screening (HTS) data for antimalarial activity has become available in recent years. This includes both phenotypic and target-based activity. Realising the maximum value of these data remains a challenge. In this r...
Annual review of pharmacology and toxicology
Dec 12, 2014
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the s...
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