Molecular Dynamics Simulation Software Market Size

Molecular Dynamics Simulation Software Market size was valued at USD 431.82 Million in 2023 and is set to exceed USD 1.42 Billion by the end of 2036, expanding at over 9.6% CAGR during the forecast period i.e., between 2024-2036. In the year 2024, the industry size of molecular dynamics simulation software is evaluated at USD 469.13 Million. The growth of the market can be attributed to growth in development of drug all around the globe. The prevalence of different disease including cardiovascular and CNS-related disorders is growing rapidly. According to the World Health Organization, 17.9 million deaths worldwide in 2019 were attributable to CVDs, or 32% of all fatalities. Heart attack and stroke deaths accounted for 85% of these fatalities. Hence the need for development of best medications, and need to improve the effectiveness and transparency of drug development is driving the growth of drug development market. This growth is hence further anticipated to boost the market for molecular dynamics simulation software.

Additionally, growing government recognition of the importance of drug discovery and increased funding for global research initiatives, notably as a result of the COVID-19 pandemic, are predicted to be major factors in market growth. For instance, Drug Development Hackathon 2020 (DDH2020), a pioneering national initiative to support the drug discovery process, was introduced by the Indian government. The first of its kind in the country, this hackathon brought together experts, academics, researchers, and students from a wide range of disciplines, including computer science, chemistry, pharmacy, medical sciences, basic sciences, and biotechnology. Additionally, machine learning has fast become an essential tool for managing and understanding enormous amounts of data across many scientific disciplines. Moreover, ML models support precise forecasting of complex material qualities, pharmaceutical development & manufacture, biosimilars, and other factors. The modelling and optimization of materials for heat treatment, resistance, microstructure models, and more could benefit from using data from earlier experimental and theoretical investigations. Therefore, it is anticipated that the market would experience profitable prospects as a result of the expanding potential of machine learning models in molecular dynamics simulations.