My advice is to go far to Schrodinger (Nasdaq:SDGR). At first glance, SDGR may appear to be a difficult biotechnology company to value due to the binary nature of the industry. However, the main value of business comes from its computing platform that delivers an extremely high value proposition that meets the critical needs of biopharmaceutical companies. I believe SDGR’s deep experience and years of computing data are key differentiators for its software, which should allow it to continue to pick up share.
The primary focus of SDGR is the pharmaceutical industry, specifically drug discovery, material design software development and management of stand-alone and collaborative drug discovery initiatives. Since I am not a biomedical expert, my focus will be on the SDGR software business.
Strong SDGR software business valuable site
The majority of revenue comes from licensing programs that the company develops and sells to the biopharmaceutical industry around the world for use in drug discovery. Initially lacking in technical prowess, SDGR has expanded its offering to include a comprehensive suite of software and services for the entire drug design process. A variety of clients use the service, from small biotech companies to multinational pharma.
The company’s software products are in high demand because they are useful tools for biotechnology and pharmaceutical companies involved in drug discovery and development. Clients are not limited to those in the medical industry; Instead, they also involve people in the advanced materials sector who work to overcome fundamental challenges such as long, capital-intensive processes with high risks of failure. With regard to drugs, the difficulties with chemistry are complex. It is very difficult to make accurate projections into the future given the complexity of all of these factors in drug design. Several decades of efforts have paid off in SDGR’s solid brand name and competitive edge in the market for its software suite. Scientists and software engineers at SDGR were instrumental in creating a computational platform with the ability to accurately predict drug properties.
SDGR’s software business is primarily dependent on these factors:
- To attract new customers
- Expanding the Company’s use of its pipeline products among its existing customer base
- To expand the company’s customer base in new markets.
SDGR recurring charges are calculated by the number of particles a user explores at one time and the length of time the user explores. The demand for more effective research and development in biopharmaceutical companies in order to identify high-quality drug candidates is a major driver of the SDGR programme. Because the number of potential molecules is so large, this is important because factors such as computational power often limit how efficiently they can be explored. Software developed by SDGR allows users to quickly and accurately assess the properties of a large number of molecules, potentially in the billions. Comparatively, conventional methods may require fewer molecules to be synthesized over a much longer period of time.
A differentiated product stemming from years of data processing
The SDGR platform is built on a solid foundation of physics-based methods, harnesses data-processing muscles, and harnesses the scaling benefits of machine learning. Computing power improvements, better understanding of the model, and advances in high-resolution protein structure analysis have contributed to this development over time. These allowed SDGR to create a unique physics-based simulation-based software platform.
Other use cases in materials science
Companies from the materials science industry looking to create new molecules for use in industry are among buyers of SDGR software licenses. Aerospace, semiconductors, and the energy sector are just some of the industries SDGR serves. I think there is room for growth in the use of computational methods for molecular discovery among these clients, as they use a lot of the same technology as the life sciences clients. However, the proportion of the company’s revenue and customers attributed to this focus is relatively small.
Predict the growth of the software business
The amount of money spent on research and development in life sciences is an important factor affecting demand dynamics. My concern is that funding opportunities for startups in the biotech industry have become more difficult in recent years due to the general economic climate. However, I believe SDGR’s value proposition wins here, and customers will remain invested in the SDGR platform because of the savings they get compared to real-world experiences.
In short, customers from the biopharmaceutical industry are the primary force behind the expansion of the software division. I think there is growing interest In the application of artificial intelligence and machine learning in the biopharmaceutical industry, mainly driven by the need to develop new drugs. To this end, I believe SDGR is in a great position to leverage their existing partnerships in order to further expand their technology capabilities, given their position as an established player and leader in the adoption of physics-based methods in machine learning.
short term growth
In the near future, I expect SDGR to reap the benefits of its well-established competitive advantage in the pharmaceutical industry. Underpinning this perspective is the long-standing computing platform, loyal customer base, and successful track record that SDGR enjoys through its partnerships. In addition to qualitative evidence, such as an increase in the number of programs progressing to the next stage, SDGR has provided quantitative measures to demonstrate its success in simplifying drug discovery. In addition, SDGR also has a very high retention rate (>97%). I think all of this is clear evidence of success.
assessment and model
Using consensus estimates, I believe the SDGR is $29.7 in FY ’23. My model focused solely on evaluating the platform business – which I believe has the most valuable offering and can be designed with a lot of certainty (as opposed to the drug development business which has binary outcomes).
Now, the question is what valuation multiple should a company with a strong value proposition, high retention rates, high gross margin, and growth prospects trade at? I think it’s much higher than 4.8x but to be conservative I assumed it would trade at that level.
There is technological competition from other companies offering computational platforms that increasingly use physics-based methods. While the underlying technology for all machine learning applications is the same, the careful selection of training inputs and final application is what sets each company apart. Although SDGR has been ahead of the curve in this area, competitors such as Exscientia (EXAI) are starting to see the potential and offer the feature on their own platforms.
At first glance, SDGR may seem like a difficult biotechnology company to value due to the binary nature of the industry. However, the company’s core value stems from its cutting-edge computing platform, which delivers a compelling value proposition and meets the demanding demands of the biopharmaceutical industry. I believe that SDGR’s extensive background and years of computing data are major selling points for its software and will allow it to maintain its market dominance.