Drug Formulation Development
Pharmaceutical formulation data include formulation compositions and manufacturing process. One of
the main difficulties in formulation prediction is the small dataset with imbalanced input space resulting in
overfitting and poor generalizations, because of the limited and unstandardized experimental data.
Artificial intelligent methods can find out the intricate correlation between pharmaceutical formulations
and in vitro/vivo characteristics.
AI-driven formulation platform supported by MedAI, is to enable targeted, smart novel drug candidates.
Through the integration of machine learning, deep learning, quantum simulation, and high-throughput
experimentation, our experts enable formulation scientists to rapidly, comprehensively and intelligently
develop clinically differentiable products. The cross-disciplinary integration of pharmaceutics and artificial
intelligence may shift the paradigm of pharmaceutical researches from experience-dependent studies to
data-driven methodologies. These intelligent ways of working fundamentally transform drug
development, and drug product lifecycle management, and ultimately bring more quality drug products to
patients. AI methods, like artificial neural networks (ANNs) and deep learning strategies, can greatly
speed the development, optimize formulations, save the cost, and keep products consistency.
Figure 1 Drug Formulation Development Platform
SOLUTIONS PLATFORM CAREERS
Please input your keywords...
Iterative Learning Cycle of AI-driven Drug Formulation
Pharmaceutical datasets (formulation and experimental data extracted from Web of Science, training /
validation / test datasets), and datasets selectionGeneration of massive volumes of highly accurate
semantically consistent observational facts in the biomedical literature and other sources. Develop pre-
curated vocabularies to enable lexical matching and to deal with the synonym variations across the data
Molecular descriptors (molecular weight, XLogP3, hydr