AIMC Topic: Drug Discovery
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MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.
BMC biology
Aug 26, 2024
BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments b...
A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish.
Journal of neuroscience methods
Aug 24, 2024
BACKGROUND: Although zebrafish are increasingly utilized in biomedicine for CNS disease modelling and drug discovery, this generates big data necessitating objective, precise and reproducible analyses. The artificial intelligence (AI) applications ha...
Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance.
Chemistry, an Asian journal
Aug 23, 2024
Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antib...
SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.
Journal of chemical information and modeling
Aug 23, 2024
Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into...
Accelerating drug discovery, development, and clinical trials by artificial intelligence.
Med (New York, N.Y.)
Aug 21, 2024
Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation in drug development. This paper aims to deliver a comprehensive analysis of the progress in AI-assis...
Deep learning large-scale drug discovery and repurposing.
Nature computational science
Aug 21, 2024
Large-scale drug discovery and repurposing is challenging. Identifying the mechanism of action (MOA) is crucial, yet current approaches are costly and low-throughput. Here we present an approach for MOA identification by profiling changes in mitochon...
Perspectives on the use of machine learning for ADME prediction at AstraZeneca.
Xenobiotica; the fate of foreign compounds in biological systems
Aug 21, 2024
A drug's pharmacokinetic (PK) profile will determine its dose and the frequency of administration as well as the likelihood of observing any adverse drug reactions.It is important to understand these PK properties as early as possible in the drug dis...
Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.
Journal of computer-aided molecular design
Aug 20, 2024
The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success a...
MalariaFlow: A comprehensive deep learning platform for multistage phenotypic antimalarial drug discovery.
European journal of medicinal chemistry
Aug 16, 2024
Malaria remains a significant global health challenge due to the growing drug resistance of Plasmodium parasites and the failure to block transmission within human host. While machine learning (ML) and deep learning (DL) methods have shown promise in...
FitScore: a fast machine learning-based score for 3D virtual screening enrichment.
Journal of computer-aided molecular design
Aug 16, 2024
Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large d...
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