Biosimulation, which uses computer simulations of biological processes to predict the behavior of biological systems, is on a significant growth trajectory – thanks in part to the FDA’s strong recommendation for adopting biosimulation, an overall increase in predictive biosimulation in the research and development (R&D) process, and the recent use of biosimulation platforms for the development of COVID-19 vaccines. Now, as pharmaceutical and biotech companies continue to invest in artificial intelligence (AI)-enabled tools and technologies, we should expect to see even more widespread adoption of biosimulation across drug discovery and development.
Biosimulation, which uses computer simulations of biological processes to predict the behavior of biological systems, is on a significant growth trajectory – thanks in part to the FDA’s strong recommendation for adopting biosimulation, an overall increase in predictive biosimulation in the research and development (R&D) process, and the recent use of biosimulation platforms for the development of COVID-19 vaccines. Now, as pharmaceutical and biotech companies continue to invest in artificial intelligence (AI)-enabled tools and technologies, we should expect to see even more widespread adoption of biosimulation across drug discovery and development.
Mechanistic models serve as a useful candidate for modeling the detailed inner workings of a system in a true-to-life manner, going above and beyond simulating basic input and output. They can be used as a tool to improve preclinical translatability, reduce time and cost investments, and solve real-world problems safely.
Mechanistic models serve as a useful candidate for modeling the detailed inner workings of a system in a true-to-life manner, going above and beyond simulating basic input and output. They can be used as a tool to improve preclinical translatability, reduce time and cost investments, and solve real-world problems safely.
AI technologies like deep learning, machine learning and natural language processing have the potential to address many of the challenges that traditionally plague drug R&D – accelerating molecule design and testing, streamlining essential processes, improving chances of clinical success, and reducing costs throughout the development pipeline.
AI technologies like deep learning, machine learning and natural language processing have the potential to address many of the challenges that traditionally plague drug R&D – accelerating molecule design and testing, streamlining essential processes, improving chances of clinical success, and reducing costs throughout the development pipeline.
BIOiSIM is a first-in-class decision-making engine that can take your toughest questions and generate answers to guide your next steps at any stage of drug development. With the acquisition of Molomics, it will be powered by one of the largest and fastest growing inferential search spaces in existence, encompassing over 1 trillion compounds.
BIOiSIM is a first-in-class decision-making engine that can take your toughest questions and generate answers to guide your next steps at any stage of drug development. With the acquisition of Molomics, it will be powered by one of the largest and fastest growing inferential search spaces in existence, encompassing over 1 trillion compounds.