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Drug Development

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Multitype Perception Method for Drug-Target Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
With the growing popularity of artificial intelligence in drug discovery, many deep-learning technologies have been used to automatically predict unknown drug-target interactions (DTIs). A unique challenge in using these technologies to predict DTI i...

Learning image representations for anomaly detection: Application to discovery of histological alterations in drug development.

Medical image analysis
We present a system for anomaly detection in histopathological images. In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Under such settings, one-class classifiers trained on health...

Emerging drug interaction prediction enabled by a flow-based graph neural network with biomedical network.

Nature computational science
Drug-drug interactions (DDIs) for emerging drugs offer possibilities for treating and alleviating diseases, and accurately predicting these with computational methods can improve patient care and contribute to efficient drug development. However, man...

VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search.

Journal of chemical information and modeling
Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity....

EGeRepDR: An enhanced genetic-based representation learning for drug repurposing using multiple biomedical sources.

Journal of biomedical informatics
MOTIVATION: Drug repurposing (DR) is an imminent approach for identifying novel therapeutic indications for the available drugs and discovering novel drugs for previously untreatable diseases. Nowadays, DR has major attention in the pharmaceutical in...

Artificial intelligence for dementia prevention.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical t...

Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Bioorganic chemistry
Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficie...

Deep learning driven de novo drug design based on gastric proton pump structures.

Communications biology
Existing drugs often suffer in their effectiveness due to detrimental side effects, low binding affinity or pharmacokinetic problems. This may be overcome by the development of distinct compounds. Here, we exploit the rich structural basis of drug-bo...

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges.

Drug development research
By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduc...