Predicting Drug Compound Ratios With Deep Learning
Predicting Drug Compound Ratios With Deep Learning Lessons from developing ML models at CIMSEPP to optimize API and excipient formulations Formulating a drug is not just chemistry. It is an engineering problem that balances performance, stability, safety, manufacturability, and cost. At the center of that challenge sits a deceptively complex question: What is the right ratio of active pharmaceutical ingredient (API) to excipients to create a formulation that works reliably every single time? Traditionally, answering this involves iterative lab experimentation: mix, test, adjust, repeat. While this approach is scientifically sound, it is also expensive, slow, and often limited in the number of combinations a team can realistically explore. This is where deep learning can help. Deep learning does not replace formulation science. Instead, it can act as a powerful accelerator by learning patterns from historical experiments and predicting promising compound ratios before committing...