Prescriptions

Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors.

Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and development, providing advanced tools for promoting cost-effectiveness throughout drug life cycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patient...

Artificial Intelligence in Drug Safety and Metabolism.

The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligen...

Artificial Intelligence (AI) in Drugs and Pharmaceuticals.

The advancement of computing and technology has invaded all the dimensions of science. Artificial intelligence (AI) is one core branch of Computer Sci...

Harnessing the potential of machine learning for advancing "Quality by Design" in biomanufacturing.

Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs...

[Development of Clinical Pharmaceutical Services via Artificial Intelligence Adaptation].

Recently, social implementations of artificial intelligence (AI) have been rapidly advancing. Many papers have investigated the use of AI in the field...

Application of Artificial Intelligence in Drug Discovery.

Due to the heap of data sets available for drug discovery, modern drug discovery has taken the shape of big data. Usage of Artificial intelligence (AI...

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Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis ...

The potential applications of artificial intelligence in drug discovery and development.

Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological stu...

Artificial intelligence unifies knowledge and actions in drug repositioning.

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attr...

Decoding the effects of synonymous variants.

Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological ...

DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug-target interactions.

MOTIVATION: In silico drug-target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can pr...

Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.

Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene sign...

Evaluation of a Medication Robot Through Participatory Design Methods - A Case Study in Denmark.

The demographics in Denmark are changing. People's life expectancy is increasing, which puts a strain on the home care resources. The aim of this arti...

An effective self-supervised framework for learning expressive molecular global representations to drug discovery.

How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network ...

SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug-d...

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. T...

Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.

Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less toxicity and adverse side effects. However, it is...

Machine learning approaches for drug combination therapies.

Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug ...

Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery.

The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great i...

Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.

The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets s...