Prescriptions

Improved prediction of drug-target interactions based on ensemble learning with fuzzy local ternary pattern.

: The prediction of interacting drug-target pairs plays an essential role in the field of drug repurposing, and drug discovery. Although biotechnology and chemical technology have made extraordinary progress, the process of dose-response experiments and clinical trials is still extremely complex, laborious, and costly. As a result, a robust computer-aided model is of an urgent need to predict drug...

Towards realizing the vision of precision medicine: AI based prediction of clinical drug response.

Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We used the anti-epileptic drug brivaracetam as a case study and comb...

Improving the accuracy and convergence of drug permeation simulations via machine-learned collective variables.

Understanding the permeation of biomolecules through cellular membranes is critical for many biotechnological applications, including targeted drug de...

Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach.

Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like...

Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings.

An interaction between pharmacological agents can trigger unexpected adverse events. Capturing richer and more comprehensive information about drug-dr...

First Steps to Evaluate an NLP Tool's Medication Extraction Accuracy from Discharge Letters.

INTRODUCTION: The aim of this study is to evaluate the use of a natural language processing (NLP) software to extract medication statements from unstr...

GraphDTA: predicting drug-target binding affinity with graph neural networks.

SUMMARY: The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and...

CATH functional families predict functional sites in proteins.

MOTIVATION: Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Sever...

Beware of the generic machine learning-based scoring functions in structure-based virtual screening.

Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for stru...

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.

Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. Th...

Machines Like Us and People Like You: Toward Human-Robot Shared Experience.

In the past years, the field of collaborative robots has been developing fast, with applications ranging from health care to search and rescue, constr...

[Exploration on rationality evaluation approach of drug combination medication based on sequential analysis and machine learning].

Drug combination is a common clinical phenomenon. However, the scientific implementation of drug combination is li-mited by the weak rational evaluati...

Prediction of drug adverse events using deep learning in pharmaceutical discovery.

Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-i...

Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records.

OBJECTIVE: To develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic he...

Machine learning-integrated omics for the risk and safety assessment of nanomaterials.

With the advancement in nanotechnology, we are experiencing transformation in world order with deep insemination of nanoproducts from basic necessitie...

Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States.

This study examines the role that racial residential segregation has played in shaping the spread of COVID-19 in the United States as of September 30,...

A deep learning-based medication behavior monitoring system.

The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical...

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists.

Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases...

A survey on adverse drug reaction studies: data, tasks and machine learning methods.

MOTIVATION: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both c...

Deep learning for drug response prediction in cancer.

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learnin...