AIMC Topic: Drug Development
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Artificial intelligence in pharmaceutical research and development.
Future medicinal chemistry
Jul 3, 2018
A meta-learning framework using representation learning to predict drug-drug interaction.
Journal of biomedical informatics
Jun 26, 2018
MOTIVATION: Predicting Drug-Drug Interaction (DDI) has become a crucial step in the drug discovery and development process, owing to the rise in the number of drugs co-administered with other drugs. Consequently, the usage of computational methods fo...
Drug Target Prediction by Multi-View Low Rank Embedding.
IEEE/ACM transactions on computational biology and bioinformatics
May 18, 2017
Drug repositioning has been a key problem in drug development, and heterogeneous data sources are used to predict drug-target interactions by different approaches. However, most of studies focus on a single representation of drugs or proteins. It has...
AI Drug Discovery: Expanding the Horizons of Infectious Disease Therapeutics.
ACS infectious diseases
Aug 8, 2025
Drug discovery and development for infectious diseases has transformed from phenotypic screening to rational design, and now embracing artificial intelligence (AI) to accelerate and optimize therapeutic development. We describe our use of AI to analy...
Harnessing computational technologies to facilitate antibody-drug conjugate development.
Nature chemical biology
Aug 1, 2025
Antibody-drug conjugates (ADCs) represent a powerful therapeutic approach for the treatment of a range of cancers. They merge the toxicity of known chemical agents with the specificity of monoclonal antibodies, thereby maximizing efficacy while minim...
In silico design strategies for tubulin inhibitors for the development of anticancer therapies.
Expert opinion on drug discovery
Aug 1, 2025
INTRODUCTION: Microtubules, composing of α, β-tubulin dimers, are important for cellular processes like proliferation and transport, thereby they become suitable targets for research in cancer. Existing candidates often exhibit off-target effects, ne...
Application of Artificial Intelligence in the Development of Traditional Chinese Medicine.
Basic & clinical pharmacology & toxicology
Jul 1, 2025
Traditional Chinese medicine (TCM) has long been recognized for its mild therapeutic effects, significant efficacy and minimal adverse reactions. However, challenges such as reliance on human expertise in TCM production and quality control, unclear c...
The dawn of a new era: can machine learning and large language models reshape QSP modeling?
Journal of pharmacokinetics and pharmacodynamics
Jun 16, 2025
Quantitative Systems Pharmacology (QSP) has emerged as a cornerstone of modern drug development, providing a robust framework to integrate data from preclinical and clinical studies, enhance decision-making, and optimize therapeutic strategies. By mo...
DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation.
Nature communications
May 30, 2025
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug disco...
What patents on AI-derived drugs reveal.
Science (New York, N.Y.)
May 29, 2025
Less in-depth, in vivo testing before patenting may affect overall research and development.
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