AIMC Topic: Drug Development

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Increasing acceptance of AI-generated digital twins through clinical trial applications.

Clinical and translational science
Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the ...

Accelerating Drug Development Using Spatial Multi-omics.

Cancer discovery
Spatial biology approaches enabled by innovations in imaging biomarker platforms and artificial intelligence-enabled data integration and analysis provide an assessment of patient and disease heterogeneity at ever-increasing resolution. The utility o...

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications.

Drug development research
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and exp...

ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Briefings in bioinformatics
Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 5...

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.

Current pharmaceutical design
Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neura...

Application of Deep Learning for Studying NMDA Receptors.

Methods in molecular biology (Clifton, N.J.)
Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, su...

Developing a GNN-based AI model to predict mitochondrial toxicity using the bagging method.

The Journal of toxicological sciences
Mitochondrial toxicity has been implicated in the development of various toxicities, including hepatotoxicity. Therefore, mitochondrial toxicity has become a major screening factor in the early discovery phase of drug development. Several models have...

LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach.

Current pharmaceutical design
INTRODUCTION: Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain re...

Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.

Current topics in medicinal chemistry
BACKGROUND: SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and t...