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 Protheragen, 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
Table 1 Recent progress of machine learning in formulation design (Yilong Yang, et al. 2018)
Based on numerous data, intelligent tools have been developed implementing bioinformatics and machine learning methods for drug research and discovery. Protheragen also offers biomarker discovery and targeted proteomics services to researchers that want to benefit from our technology.
Figure 1 The workflow of MD-FIS algorithm (Yilong Yang, et al. 2018)
AI & Medicine, a division of Protheragen, provides AI-powered solutions for drug formulation development according to clients' detailed requirements. Our platform improves the efficiency and success rate of drug discovery and development with the help of advanced technologies.
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