AIMC Topic: Drug Development

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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...

DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network.

Journal of computational biology : a journal of computational molecular cell biology
In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjac...

FDA Modernization Act 2.0: An insight from nondeveloping country.

Drug development research
Animal testing is required in drug development research and is crucial for assessing the efficacy and safety of medications before they are commercialized. However, the newly furnished Food and Drug Administration Modernization Act 2.0 has given new ...

Organ-on-a-chip meets artificial intelligence in drug evaluation.

Theranostics
Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-...