AIMC Topic: Drug Development

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A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions.

Molecules (Basel, Switzerland) Feb 3, 2023
The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (K...
Algorithms Molecular Docking Simulation Deep Learning Drug Development Drug Interactions
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Pharma's Bio-AI revolution.

Drug discovery today Feb 2, 2023
Drug development has become unbearably slow and expensive. A key underlying problem is the clinical prediction challenge: the inability to predict which drug candidates will be safe in the human body and for whom. Recently, a dramatic regulatory chan...
Humans Artificial Intelligence Machine Learning Animals Drug Development
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miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies.

Biomolecules Dec 23, 2022
The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural...
Neural Networks, Computer Machine Learning Drug Discovery Drug Development Benchmarking
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A Mutual Attention Model for Drug Target Binding Affinity Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2022
Vrious machine learning approaches have been developed for drug-target interaction (DTI) prediction. One class of these approaches, DTBA, is interested in Drug-Target Binding Affinity strength, rather than focusing merely on the presence or absence o...
Machine Learning Drug Development
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DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model.

IEEE/ACM transactions on computational biology and bioinformatics Oct 10, 2022
Identification of drug-target interaction (DTI) is the most important issue in the broad field of drug discovery. Using purely biological experiments to verify drug-target binding profiles takes lots of time and effort, so computational technologies ...
Neural Networks, Computer Drug Discovery Deep Learning Drug Development Proteins
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Premexotac: Machine learning bitterants predictor for advancing pharmaceutical development.

International journal of pharmaceutics Oct 5, 2022
Bitter taste receptors were recently found to be involved in numerous physiological and pathological conditions other than taste and are suggested as potential drug targets. In vivo and in vitro techniques for screening bitterants as ligands come wit...
Machine Learning Algorithms Drug Development Ligands Taste Aversive Agents
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Drug-target binding affinity prediction method based on a deep graph neural network.

Mathematical biosciences and engineering : MBE Sep 30, 2022
The development of new drugs is a long and costly process, Computer-aided drug design reduces development costs while computationally shortening the new drug development cycle, in which DTA (Drug-Target binding Affinity) prediction is a key step to s...
Neural Networks, Computer Drug Design Drug Development Proteins
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MultiscaleDTA: A multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction.

Methods (San Diego, Calif.) Sep 23, 2022
The task of predicting drug-target affinity (DTA) plays an increasingly important role in the early stage of in silico drug discovery and development. Currently, a variety of machine learning-based methods have been presented for DTA prediction and a...
Neural Networks, Computer Machine Learning Drug Discovery Drug Development
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Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism.

International journal of molecular sciences Sep 22, 2022
The prediction of the strengths of drug-target interactions, also called drug-target binding affinities (DTA), plays a fundamental role in facilitating drug discovery, where the goal is to find prospective drug candidates. With the increase in the nu...
Machine Learning Algorithms Drug Discovery Drug Development Proteins
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GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Methods (San Diego, Calif.) Sep 1, 2022
Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based ...
Neural Networks, Computer Drug Development Drug Interactions
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