Designing new drugs and formulations with desired properties is an important and difficult problem in the pharmaceutical industry. In going from discovery to delivery in the market via several intermediate stages, a staggering ‎amount of information of different types, ranging from raw data to lab reports to sophisticated math models, is shared and revised by humans with the aid of computational tools in each stage. Traditional trial-and-error design approaches are laborious and expensive, that delay time-to-market as well as miss some potential solutions. A new paradigm is needed that increases the idea flow, broadens the search horizon, and archives the knowledge from today's successes to accelerate those of tomorrow. However, due to the incompatibility among such diverse types of data, information and knowledge, an appropriate automated decision support environment to address these needs is also very much lacking. Additionally, at present most of the information and knowledge are generated and processed directly by humans. With the onset of information and knowledge explosion, it is clear that we need intelligent software systems to effectively manage and access information for efficient decision making. Proper informatics support to drug discovery and process development is crucial in order ‎to achieve speed to market, and getting the process right ‎the first time. In addition, a systematic way to convert the raw data ‎gathered from process analytical technologies (PAT) to information and first principles knowledge that can be used ‎for real-time decision making is also lacking. All these present considerable challenges, as well as great opportunities, for a new model-based informatics framework to address these important problems. In this talk, I will review the state of the art and present an overview of emerging trends in developing a novel cyberinfrastructure framework that enables the management of complexity, accumulation of knowledge, systematic hypotheses testing by interaction with experiments, and optimal decision-making.