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Prescriptions

Latest AI and machine learning research in prescriptions for healthcare professionals.

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Large language models for preventing medication direction errors in online pharmacies.

Errors in pharmacy medication directions, such as incorrect instructions for dosage or frequency, ca...

Accuracy of a chatbot in answering questions that patients should ask before taking a new medication.

BACKGROUND: The potential uses of artificial intelligence have extended into the fields of health ca...

Unleashing the power of generative AI in drug discovery.

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing time...

[The revolution of AI in drug development].

Artificial intelligence and machine learning enable the construction of predictive models, which are...

Prospective de novo drug design with deep interactome learning.

De novo drug design aims to generate molecules from scratch that possess specific chemical and pharm...

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites ...

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue.

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However...

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites.

This study delves into the prediction of protein-peptide interactions using advanced machine learnin...

DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning.

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and co...

Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning.

Predicting the probability of having the plan approved by the physician is important for automatic t...

Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes.

At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug r...

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling.

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, po...

Analyzing to discover origins of CNNs and ViT architectures in medical images.

In this paper, we introduce in-depth the analysis of CNNs and ViT architectures in medical images, w...

SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.

Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field...

The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning.

Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Desp...

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological r...

AI-driven design of customized 3D-printed multi-layer capsules with controlled drug release profiles for personalized medicine.

Personalized medicine aims to effectively and efficiently provide customized drugs that cater to div...

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