AIMC Topic: Drug Development

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Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure.

Genomics
The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning method...

Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective.

IEEE reviews in biomedical engineering
With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical tr...

A meta-learning framework using representation learning to predict drug-drug interaction.

Journal of biomedical informatics
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
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...

Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning.

mAbs
Highly concentrated antibody solutions are necessary for developing subcutaneous injections but often exhibit high viscosities, posing challenges in antibody-drug development, manufacturing, and administration. Previous computational models were only...

Role of artificial intelligence in cancer drug discovery and development.

Cancer letters
The role of artificial intelligence (AI) in cancer drug discovery and development has garnered significant attention due to its potential to transform the traditionally time-consuming and expensive processes involved in bringing new therapies to mark...

AI Drug Discovery: Expanding the Horizons of Infectious Disease Therapeutics.

ACS infectious diseases
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
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...