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Databases, Pharmaceutical

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Drug knowledge bases and their applications in biomedical informatics research.

Briefings in bioinformatics
Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many...

DrugR+: A comprehensive relational database for drug repurposing, combination therapy, and replacement therapy.

Computers in biology and medicine
Drug repurposing or repositioning, which introduces new applications of the existing drugs, is an emerging field in drug discovery scope. To enhance the success rate of the research and development (R&D) process in a cost- and time-effective manner, ...

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods.

Journal of chemical information and modeling
This work reports the classification study conducted on the biggest COX-2 inhibitor data set so far. Using 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, we applied machine learning methods, support vector machine (SVM) and ra...

Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer's Disease.

Journal of chemical information and modeling
It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) database, a network-pharmacology-based approach was utilized to investigate TCM ...

Predicting drug synergy for precision medicine using network biology and machine learning.

Journal of bioinformatics and computational biology
Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effecti...

Imputation of Assay Bioactivity Data Using Deep Learning.

Journal of chemical information and modeling
We describe a novel deep learning neural network method and its application to impute assay pIC values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and c...

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Journal of chemical information and modeling
Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds...

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets.

Journal of chemical information and modeling
Successful drug discovery projects require control and optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety. While volume and chemotype coverage of public and corporate ADME-Tox (absorption, distribution, excr...

De Novo Molecule Design by Translating from Reduced Graphs to SMILES.

Journal of chemical information and modeling
A key component of automated molecular design is the generation of compound ideas for subsequent filtering and assessment. Recently deep learning approaches have been explored as alternatives to traditional de novo molecular design techniques. Deep l...