The North America drug modelling software market is expected to reach US$ 4,588.87 million by 2027 from US$ 2,468.17 million in 2019. The market is estimated to grow at a CAGR of 8.4% during 2020-2027.
The drug modelling software market is growing primarily due to increasing adoption of in-silico modelling tools in drug discovery and rising economic burden of drug discovery in North America. Additionally, growing adoption of artificial intelligence in drug discovery and strategic activities by market players are likely to fuel the growth of the drug modelling software market during the forecast period. However, factors such as lack of data standardization are likely to restrain the growth of the market.
Drug modelling has become an essential tool in the drug design process. Software-based drug discovery and development methods are playing a key role in the development of novel drugs. Software-based methods, such structure-based virtual screening, structure-based drug design, as molecular modelling, ligand interaction, and molecular dynamics, are considered to be a powerful tool for investigation of pharmacodynamic and pharmacokinetic properties of drugs. These methods are fast and accurate. They provide valuable insights of experimental findings and mechanisms of action. In addition, appropriate implementation of these techniques may help in reducing cost of drug designing and development.
Traditional drug discovery and development method is capital-intensive and time-consuming. Moreover, it has high failure rates. It involves experimental screening of existing libraries of molecules followed by many rounds of chemical synthesis. The process of drug development, from the discovery of a lead compound to its commercial launch, is estimated to take around 10-15 years along with the investment of a huge amount of money. As per the Pharmaceutical Research and Manufacturers of America, the average cost to research and develop a successful drug is estimated to be US$ 2.6 billion. Moreover, only a small proportion of leads that are selected for further investigation during the initial stages of research are translated into clinical research studies. Over a period, the complexities of drug discovery have increased owing to the increasing size of biologics. As a result, there has been a direct rise in R&D expenditure in the pharmaceutical sector. As per the Pharmaceutical Research and Manufacturers of America, in 2017, biopharmaceutical companies sponsored more than 4,500 clinical trials in the US. These trials accounted for approximately US$ 43 billion; moreover, in 2017, biopharmaceutical companies in the US invested around US$ 97 billion in R&D. At present, the pharmaceutical industry is under tremendous pressure to cope with rising capital requirements in drug discovery research and avoid losses due to drug failure.
In the past few years, several computational tools have been developed for the identification, selection, and optimization of pharmacological lead candidates. Currently, there are several computational approaches available for the drug discovery process. The predictive power of these tools has been proven to be very advantageous, allowing researchers to bypass the screening of billions of molecules. As a result, computational services, such as quantitative structure-activity relationship (QSAR), modelling, and computer-aided drug design (CADD), have now become an integral part of the pharmaceutical industry. Moreover, pharmaceutical companies that are focused on the development of large molecules are likely to continue outsourcing their respective drug discovery and development operations from drug modelling providers.
Drugs are essential to curb COVID-19; more than 206,000 people have tragically died from the disease. In the U. S., to give the drug modelling technology a push, ImmunityBio and Microsoft collaborated to support for the research. Supporting COVID-19 vaccine and drug development is the main aim of ImmunityBio’s collaboration with Microsoft to leverage the latter’s Azure platform to create a 3D model of SARS-CoV-2’s spike protein. Additionally, Research projects and innovations related to COVID-19 have ramped up quickly across the University of Michigan, spurred by doctors, public health experts, scientists, economists, and engineers; and encouraged by research leaders. With the emerging outbreak of the COVID-19 pandemic, the drug modelling market is likely to be positively impacted.
In 2019, the software segment based on product type accounted for the highest share of the market. Also, the same segment is estimated to grow at the highest CAGR during the forecast period. Growth of this segment can be attributed to rise in demand for effective therapeutics and increase in drug discovery efforts of various biologics across a wide range of therapeutics. In addition, strategic activities by service providers, such as collaborations, product advancement, and product launch, in order to accelerate drug discovery timeline are further accelerating the growth of the market.
A few of the major secondary sources associated with the North America drug modelling software market report include World Health Organization (WHO), OECD Health Statistics 2015, Pharmaceutical Research and Manufacturers of America, and Centers of Disease Control and Prevention (CDC).
Reasons to Buy
The drug modelling software market is growing primarily due to increasing adoption of in-silico modelling tools in drug discovery and rising economic burden of drug discovery in North America. Additionally, growing adoption of artificial intelligence in drug discovery and strategic activities by market players are likely to fuel the growth of the drug modelling software market during the forecast period. However, factors such as lack of data standardization are likely to restrain the growth of the market.
Drug modelling has become an essential tool in the drug design process. Software-based drug discovery and development methods are playing a key role in the development of novel drugs. Software-based methods, such structure-based virtual screening, structure-based drug design, as molecular modelling, ligand interaction, and molecular dynamics, are considered to be a powerful tool for investigation of pharmacodynamic and pharmacokinetic properties of drugs. These methods are fast and accurate. They provide valuable insights of experimental findings and mechanisms of action. In addition, appropriate implementation of these techniques may help in reducing cost of drug designing and development.
Traditional drug discovery and development method is capital-intensive and time-consuming. Moreover, it has high failure rates. It involves experimental screening of existing libraries of molecules followed by many rounds of chemical synthesis. The process of drug development, from the discovery of a lead compound to its commercial launch, is estimated to take around 10-15 years along with the investment of a huge amount of money. As per the Pharmaceutical Research and Manufacturers of America, the average cost to research and develop a successful drug is estimated to be US$ 2.6 billion. Moreover, only a small proportion of leads that are selected for further investigation during the initial stages of research are translated into clinical research studies. Over a period, the complexities of drug discovery have increased owing to the increasing size of biologics. As a result, there has been a direct rise in R&D expenditure in the pharmaceutical sector. As per the Pharmaceutical Research and Manufacturers of America, in 2017, biopharmaceutical companies sponsored more than 4,500 clinical trials in the US. These trials accounted for approximately US$ 43 billion; moreover, in 2017, biopharmaceutical companies in the US invested around US$ 97 billion in R&D. At present, the pharmaceutical industry is under tremendous pressure to cope with rising capital requirements in drug discovery research and avoid losses due to drug failure.
In the past few years, several computational tools have been developed for the identification, selection, and optimization of pharmacological lead candidates. Currently, there are several computational approaches available for the drug discovery process. The predictive power of these tools has been proven to be very advantageous, allowing researchers to bypass the screening of billions of molecules. As a result, computational services, such as quantitative structure-activity relationship (QSAR), modelling, and computer-aided drug design (CADD), have now become an integral part of the pharmaceutical industry. Moreover, pharmaceutical companies that are focused on the development of large molecules are likely to continue outsourcing their respective drug discovery and development operations from drug modelling providers.
Drugs are essential to curb COVID-19; more than 206,000 people have tragically died from the disease. In the U. S., to give the drug modelling technology a push, ImmunityBio and Microsoft collaborated to support for the research. Supporting COVID-19 vaccine and drug development is the main aim of ImmunityBio’s collaboration with Microsoft to leverage the latter’s Azure platform to create a 3D model of SARS-CoV-2’s spike protein. Additionally, Research projects and innovations related to COVID-19 have ramped up quickly across the University of Michigan, spurred by doctors, public health experts, scientists, economists, and engineers; and encouraged by research leaders. With the emerging outbreak of the COVID-19 pandemic, the drug modelling market is likely to be positively impacted.
In 2019, the software segment based on product type accounted for the highest share of the market. Also, the same segment is estimated to grow at the highest CAGR during the forecast period. Growth of this segment can be attributed to rise in demand for effective therapeutics and increase in drug discovery efforts of various biologics across a wide range of therapeutics. In addition, strategic activities by service providers, such as collaborations, product advancement, and product launch, in order to accelerate drug discovery timeline are further accelerating the growth of the market.
A few of the major secondary sources associated with the North America drug modelling software market report include World Health Organization (WHO), OECD Health Statistics 2015, Pharmaceutical Research and Manufacturers of America, and Centers of Disease Control and Prevention (CDC).
Reasons to Buy
- It provides understanding of the North America, drug modelling software market landscape and identifies drug modelling software market segments that are most likely to guarantee a strong return.
- It guides stay ahead of the race by comprehending the ever-changing competitive landscape for the North America drug modelling software market.
- It helps efficiently plan merger and acquisition, and partnership deals in the drug modelling software market by identifying market segments with the most promising probable sales.
- It facilitates knowledgeable business decision-making through perceptive and comprehensive analysis of drug modelling software market performance of various segments pertaining to the North America drug modelling software market.
- It provides drug modelling software market revenue forecast of the market based on various segments for the period from 2019 to 2027.
Table of Contents
1. Introduction
3. Research Methodology
4. North America Drug Modelling Software Market - Market Landscape
5. North America Drug Modelling Software Market - Key Market Dynamics
6. Drug Modelling Software Market - North America Analysis
7. North America Drug Modelling Software Market Analysis and Forecasts To 2027 - By Product Type
8. North America Drug Modelling Software Market Analysis and Forecasts To 2027 - By Application
9. Drug Modelling Software Market Revenue and Forecasts To 2027 - Geographical Analysis
10. Impact of COVID-19 Pandemic on North America Drug Modelling Software Market
11. Company Profiles
12. Appendix
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Crown Bioscience Inc (JSR life science)
- Chemical Computing Group ULC
- Nimbus Therapeutics
- Schrödinger, Inc
- Leadscope, Inc (Instem)
