{"id":56300,"date":"2023-09-03T00:00:49","date_gmt":"2023-09-03T04:00:49","guid":{"rendered":"https:\/\/ifintechworld.com\/news\/schrodinger-more-than-an-ai-bubble-stock-nasdaqsdgr\/"},"modified":"2023-09-03T00:00:58","modified_gmt":"2023-09-03T04:00:58","slug":"schrodinger-more-than-an-ai-bubble-stock-nasdaqsdgr","status":"publish","type":"post","link":"https:\/\/ifintechworld.com\/?p=56300","title":{"rendered":"Schrodinger: More Than An AI Bubble Stock (NASDAQ:SDGR)"},"content":{"rendered":"
\n
<\/picture>
<\/figcaption><\/figure>\n

Schrodinger\u2019s (NASDAQ:SDGR<\/span>) stock has surged this year on the back of AI hype and continued strength in its drug discovery business. Schrodinger is far more than an AI bubble stock though. As the drug discovery business matures, the importance of Schrodinger’s platform is becoming more apparent, and AI only enhances this. While the stock is not as undervalued as it was at the end of 2022, current prices probably represent a reasonably entry point for investors willing to tolerate near-term volatility.<\/span><\/p>\n

Schrodinger has developed a physics-based software platform that can be used to improve the discovery of therapeutics and materials. This platform is capable of predicting critical properties of molecules with a high degree of accuracy, enabling the discovery of novel molecules at a lower cost.<\/p>\n

\"Platform <\/picture>
\n

Table 1: Platform Use Cases (source: Created by author using data from Schrodinger)<\/span><\/p>\n<\/figcaption><\/figure>\n

Drug Discovery<\/h2>\n

Drug discovery is complex, time consuming, capital-intensive and failure rates are generally high. For example, roughly 66% of programs never succeed in delivering an IND. Part of the reason for this is that it is difficult to predict the properties of a molecule prior to chemical synthesis. The traditional approach to drug discovery has been to screen a library of molecules to identify those with detectable activity, followed by optimization of hit molecules to develop a candidate drug. Iterative synthesis and testing are time consuming and expensive though. Candidate optimization must also occur across a range of properties like potency, selectivity, solubility, bioavailability, half-life, permeability, drug-drug interaction potential, synthesizability, and toxicity. These properties are often at odds with each other, making optimization a difficult problem that often doesn\u2019t yield an adequate solution.<\/p>\n

Machine learning can help with this process, but it has limitations. For example, machine learning cannot extrapolate molecules that are dissimilar to the training set. As the design space of potential molecules is enormous and training data sets extremely limited in comparison, machine learning can only cover a small fraction of the total number of molecules.<\/p>\n

As Schrodinger\u2019s software is based on first principles, it can accurately predict the properties of novel molecules. This is a relatively slow and compute intensive process, but AI can be used as a force multiplier. Rather than trying to model billions of molecules, Schrodinger can model a smaller number of diverse molecules and use this to build training data sets across a wider expanse of the design space.<\/p>\n

While Schrodinger’s software allows more of the chemical space to be explored, even if 10^9 molecules are scored using machine learning, this is essentially nothing compared to the estimated 10^60 possible molecular combinations with drug-like characteristics in the chemical universe. It is a clear improvement though, particularly if there is sufficient diversity in the tested molecules.<\/p>\n

\"Impact<\/span>
\n

Figure 1: Impact of Schrodinger’s Platform on AI Driven Drug Discovery (source: Created by author)<\/span><\/p>\n<\/figcaption><\/figure>\n

Market<\/h2>\n

Greater access to computing power, more sophisticated algorithms and the growing availability of high-resolution protein structures have opened up the potential of physics-based software in recent years. Demand is also increasing on the back of declining drug discovery ROIs, a phenomenon that has been referred to as Eroom\u2019s Law.<\/p>\n

The global drug discovery software market opportunity was estimated to be 2 billion USD in 2020, growing at a 14% CAGR through to 2025. The drug discovery software market includes areas like ligand-based design, structure-based design, LIMS and ELN.<\/p>\n

There are thousands of biotech and pharmaceutical companies that could benefit from this type of software, but adoption is currently low. If a significant portion of these companies were to use modelling software at the same level Schrodinger reportedly does internally, the market opportunity would likely be tens of billions of dollars.<\/p>\n

Schrodinger\u2019s software faces competition from companies like:<\/p>\n