Prescriptions

Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions.

How to accurately estimate protein-ligand binding affinity remains a key challenge in computer-aided drug design (CADD). In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well with experimentally measured biological activities. In the past few years, machine learning (ML)-based SFs have gradually emerged as potential altern...

Patient satisfaction with a pharmacist-led best possible medication discharge plan via tele-robot in a remote and rural community hospital.

INTRODUCTION: Medication reconciliation (MedRec) reduces the risk of preventable medication-related adverse events (ADEs). A best possible medication discharge plan (BPMDP) is a revised list of medications a patient will take when discharged from hospital; a pharmacist review ensures accuracy. For many hospitals, on-site pharmacists are non-existent. Extension of a visual presence via a mobile rob...

Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives.

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention...

Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. H...

Robot-assisted feeding: A technical application that combines learning from demonstration and visual interaction.

BACKGROUND: The traditional meal assistance robots use human-computer interaction such as buttons, voice, and EEG. However, most of them rely on excel...

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery.

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of...

Kinematic dexterity analysis of human-robot interaction of an upper limb rehabilitation robot.

BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-r...

Deep Learning in the Quest for Compound Nomination for Fighting COVID-19.

The current COVID-19 pandemic initiated an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. It c...

The Next Generation of Machine Learning in DDIs Prediction.

Drug-drug interactions may occur when combining two or more drugs may cause some adverse events such as cardiotoxicity, central neurotoxicity, hepatot...

[Potential for Big Data Analysis Using AI in the Field of Clinical Pharmacy].

Industrial reforms utilizing artificial intelligence (AI) have advanced remarkably in recent years. The application of AI to big data analysis in the ...

Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems.

BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through phys...

Brief Survey on Machine Learning in Epistasis.

In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted...

Trends in Deep Learning for Property-driven Drug Design.

It is more pressing than ever to reduce the time and costs for the development of lead compounds in the pharmaceutical industry. The co-occurrence of ...

Xanthatin Induces Leishmanicidal Activity by Affecting Carbon Metabolism in Amastigotes.

Cutaneous leishmaniasis is caused by protozoa of the genus and spread by sandflies. The standard therapy for this ailment is the first-line medicatio...

Preparation and Preliminary Evaluation of Dual-functional Nanoparticles for MRI and siRNA Delivery.

In order to improve the transfection efficiency of gene vectors and monitor the effect of gene therapy, this study prepared a drug delivery vector wit...

Deep-LUMEN assay - human lung epithelial spheroid classification from brightfield images using deep learning.

Three-dimensional (3D) tissue models such as epithelial spheroids or organoids have become popular for pre-clinical drug studies. In contrast to 2D mo...

An omics perspective on drug target discovery platforms.

The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation...

Rapid prediction of drug inhibition under heat stress: single-photon imaging combined with a convolutional neural network.

A method of predicting cellular drug inhibition due to heat stress is presented. Black phosphorus nanosheets are used as photothermal agents to induce...

A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error.

OBJECTIVE: To improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical dec...

The Performance of an Artificial Neural Network Model in Predicting the Early Distribution Kinetics of Propofol in Morbidly Obese and Lean Subjects.

BACKGROUND: Induction of anesthesia is a phase characterized by rapid changes in both drug concentration and drug effect. Conventional mammillary comp...