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Pharmaceutical Preparations

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Pharm-AutoML: An open-source, end-to-end automated machine learning package for clinical outcome prediction.

CPT: pharmacometrics & systems pharmacology
Although there is increased interest in utilizing machine learning (ML) to support drug development, technical hurdles associated with complex algorithms have limited widespread adoption. In response, we have developed Pharm-AutoML, an open-source Py...

Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.

Molecules (Basel, Switzerland)
Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We descr...

ML-DTI: Mutual Learning Mechanism for Interpretable Drug-Target Interaction Prediction.

The journal of physical chemistry letters
Deep learning (DL) provides opportunities for the identification of drug-target interactions (DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most of the existing DL-based methods formulate the drug and tar...

Predicting drug metabolism and pharmacokinetics features of in-house compounds by a hybrid machine-learning model.

Drug metabolism and pharmacokinetics
We constructed machine learning-based pharmacokinetic prediction models with very high performance. The models were trained on 26138 and 16613 compounds involved in metabolic stability and cytochrome P450 inhibition, respectively. Because the compoun...

Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network.

Biomolecules
Network-based methods for the analysis of drug-target interactions have gained attention and rely on the paradigm that a single drug can act on multiple targets rather than a single target. In this study, we have presented a novel approach to analyze...

Local-Global Memory Neural Network for Medication Prediction.

IEEE transactions on neural networks and learning systems
Electronic medical records (EMRs) play an important role in medical data mining and sequential data learning. In this article, we propose to use a sequential neural network with dynamic content-based memories to predict future medications, given EMRs...

Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future.

International journal of pharmaceutics
Over the last two centuries, medicines have evolved from crude herbal and botanical preparations into more complex manufacturing of sophisticated drug products and dosage forms. Along with the evolution of medicines, the manufacturing practices for t...

Deep learning assisted multi-omics integration for survival and drug-response prediction in breast cancer.

BMC genomics
BACKGROUND: Survival and drug response are two highly emphasized clinical outcomes in cancer research that directs the prognosis of a cancer patient. Here, we have proposed a late multi omics integrative framework that robustly quantifies survival an...

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry.

Clinical chemistry and laboratory medicine
OBJECTIVES: Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipula...

Deep generative neural network for accurate drug response imputation.

Nature communications
Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outc...