With almost 10% of the world’s fossil fuels going towards plastic production, a viable alternative to fossil fuel-based plastics has been a key goal for the past 20 years. Bio-based polymers, obtained from renewable biomass resources, have received wide attention as the ideal replacement. These polymers have been used to create bioplastics, which are a promising and sustainable alternative to oil-based plastics and could even benefit countries that are heavily reliant on foreign oil.

This journal manuscript in ChemRxiv details the three types of bio-based polymers, their strengths and weaknesses, the latest research progress, and the trends in this field of study. Because bioplastics often face skepticism from the public, likely attributed to misinformation, this article aims to clarify the confusions and raise the awareness of bio-based polymers’ importance to sustainability.

Machine learning models supporting synthesis planning applications are largely limited to the chemistry seen in training, and the accuracy and diversity of their predictions are often diminished in sparsely populated chemical subspaces. By measuring how different datasets affect the performance of trained models, we can make stronger assertions regarding the expected coverage and novelty of synthesis planning solutions, and design datasets that will open up previously difficult areas of science. 

In this study, scientists at Bayer demonstrate the significant impact that scientist-curated reactions from the CAS Content Collection have on the predictive power of a synthesis planning model. Accuracy in prediction of outcomes in rare reaction classes increased significantly – a boost of 32 percentage points – expanding understanding into new, useful chemistry.

For unique insights into an emerging field of science, this CAS white paper presents different types of bioorthogonal reactions, applications, and trends found in the CAS Content Collection™. Bioorthogonal chemistry allows for a deeper understanding of the structure and function of our biologic systems, and highlights how drug development, delivery and imaging applications could be optimized in the future.

With over 90% of the world’s plastics production requiring fossil fuels, bio-based polymers generated from renewable sources have substantial benefits over traditional plastics – from reducing CO2 emissions, increased biodegradability, and less dependence on fossil fuels. Learn more in our landscape view of this emerging field.

The sustainability of the global patent system is under pressure from the rapid growth in patent application volume and complexity. In countries seeing the fastest growth, resulting capacity gaps delay patent examination, by years in some cases, put patent quality at risk, and threaten to slow the pace of innovation and investment. 

This white paper explores challenges and opportunities for patent offices as they seek to ensure sustainability and plan for future growth, with a focus on the application of workflow solutions enabled by AI to enhance productivity. It includes insights and learnings from a collaboration between CAS and the National Institute of Industrial Property (INPI) of Brazil to address their application backlog and improve examination workflow efficiency, resulting in significant operational improvements: 

• Up to 50% reduction in examination times
• 77% of all national applications processed required less examiner search time
• 29% of all national applications processed required little or no additional search
• Workloads were efficiently managed without adding staff 
• Examiners were freed up to focus on other priorities
• Productivity improvements contributed to a reduction of 80% in the office’s backlog

With the widely publicized carbon emissions “race to zero” target by 2050 – the interest in carbon capture, storage and reduction in climate change has been an emerging field in science that has critical implications for generations ahead. Learn more in our landscape view of this emerging research with unique insights and future opportunities.

Targeted protein degradation is a novel and quickly expanding drug discovery strategy. It is a new way of utilizing disease-causing proteins to attack severe illnesses, which has the potential to be used among more serious illnesses, like cancer or neurodegenerative diseases.

This study from the American Chemical Society utilizes the CAS Content Collection and recent research on protein degraders to provide more nuanced insight into molecular glue as a tool for new drug discovery. It analyzes the advantages and disadvantages of molecular glue as a reference to support further research.

Over the last 20 years, advances in artificial intelligence (AI), specifically machine learning, have transformed the way we approach scientific research. From mapping genome sequences and discovering new antibiotics, to modeling the impacts of climate change on Earth, and even mapping the galaxy in the search for other earth-like planets, AI is transforming research across a multitude of disciplines.

Chemistry is one such area of science making huge leaps in the adoption of AI. Our latest whitepaper, "Artificial Intelligence in Chemistry: Current Landscape and Future Opportunities", explores the connection between AI and chemistry using our own technologies to map the publication and patent landscapes. We have uncovered the areas of chemistry that are leading the field with AI and those with great potential yet to be unlocked by the adoption of AI technology.

Where has AI in chemistry grown?

The number of chemistry publications and patents involving AI has exploded, with a six-fold increase observed in the period from 2015 to 2020. We have identified the major disciplines contributing to AI-related publications and patents, and compared them to understand which areas are capitalizing on this emerging technology. Disciplines leading in AI adoption include analytical chemistry, biochemistry, and industrial chemistry and chemical engineering, while areas with an opportunity for AI adoption include natural products and organic chemistry (Figure 1).

We explored the relationships between these publications and patents from 2000 to 2020 to understand how using AI has helped researchers solve problems (Figure 2). For example, between the early 2000s and 2014, the focus of AI publications and patents shifted from exploring disease diagnoses in humans to genetic algorithms and applying these to drug discovery and microRNA.

More recently, as the types of problems requiring solutions have changed, publications and patents have shifted more towards DNA methylation and cancer. Even more recently, the focus has trended towards drug discovery related to COVID-19.

Not surprisingly, our research also identified that small molecules were the biggest focus of the AI publications and patents analyzed. These encompass topics in drug discovery, retrosynthesis, and reaction optimization, reflecting where there is typically more investment from pharmaceutical companies. 

Where are the opportunities for machine learning in chemistry?

In our analysis of more than 70,000 publications, we examined interdisciplinary contributions, noting primary and secondary disciplines (Figure 3). This allowed us to plot every discipline onto a heat map, on which the color intensity reflects the strength of contribution for each discipline. At a glance, we can see the areas of study within chemistry that are leading the way with AI and those with unrealized potential.

For example, multidisciplinary publications are more common in analytical chemistry and biochemistry, where machine learning algorithms are being used to improve analysis of proteins, peptides, lipids, and nucleic acids, as well as predict chemical reactions or even discover new molecules. AI is also being widely used in materials science and physical chemistry, where the two disciplines are aiming to predict functional materials, structure-property relationships, and chemical process optimization.

The barriers to adopting AI in chemistry

Leading experts discussed the potential barriers to the adoption of AI in our webinar, Artificial Intelligence in Chemistry: Current Trends and Future Opportunities.  They identified three key barriers to adopting AI in chemistry:

Data quality: Optimal predictions are dependent upon robust, high quality datasets that provide both positive and negative examples for training. Accessing, normalizing, and preparing the data is a significant challenge today for many organizations.

Technology: While improvements are being made in computing power (quantum and cloud-based approaches), there are still perceived limitations from a user perspective. However, advances in software and user interfaces today remove programming requirements to allow more scientists to utilize machine learning in their research.

Talent shortages: Data science has a well-documented talent shortage, and chemists may not understand how approachable AI is today. Increasing collaboration between chemistry and other scientific disciplines may help accelerate the integration of AI.

An opportunity for growth of machine learning in chemistry

AI and training datasets are being used to solve problems and innovate in scientific institutions across the world, presenting a significant opportunity for data analysis and drug discovery.

Our recent whitepaper has uncovered several areas of chemistry that could benefit from investing in AI technology. The barriers to adoption have never been lower and partners, such as CAS, can help with access to large, quality datasets for analysis. It is possible to solve some of the most pressing problems and take huge strides forward beyond what’s possible with traditional data analysis through the incorporation of artificial intelligence into scientific research.

Find out all about our analysis and the insights we uncovered by reading our whitepaper or contact CAS  if you have any questions about how AI technology can support your research.

The sustainability of the global patent system is under pressure from the rapid growth in patent application volume and complexity. In countries seeing the fastest growth in filings, capacity gaps delay patent prosecution, put patent quality at risk, reduce customer satisfaction, and slow the pace of innovation and investment.

Increasing application timeliness and patent quality

Reducing application delays is a common priority among patent offices as they seek to improve customer satisfaction and foster innovation. Delays in the process create legal uncertainty for inventors and cause them to be leery of making investments. One of Brazil’s foremost patent attorneys, Juliano Ryota Murakami, a partner with Gusmao & Labrunie, highlighted the risks to IP stakeholders:

“Excessive delays in patent examinations harm a country's innovation and economic development. They discourage companies from seeking legal protection for their inventions, since, when the patent is finally granted, the technology protected in it may be totally outdated and obsolete. They also generate uncertainty about the exclusivity of reproduction and the potential commercialization of inventions.”

The burden of ensuring application timeliness and patent quality falls largely on patent examiners and is dependent on their ability to quickly find relevant prior art to expedite reviews. However, prior art searches are time-consuming, requiring complex search strategies and deep subject matter expertise. 

An EPO analysis of search activity by its staff shows that a comprehensive patent application search draws on 1.3 billion technical records in 179 databases, leading to about 600 million documents appearing in search results on a monthly basis. Not surprisingly, a study by the Japan Patent Office estimated that examiners spend about 40% of their time performing prior art searches and reviewing results. Patent quality can suffer if examiners lack the time, subject matter expertise, or technical resources to easily access prior art. 

AI-enabled search solutions can play an important role across the entire patent ecosystem in improving both efficiency and patent quality. They can support faster examinations within patent offices and can allow innovators to identify prior art earlier, avoiding the time and cost of prosecution or validity challenges for bad patents. This is why the AI prior art algorithms were added to the CAS SciFinder Discovery Platform and STNext^® solutions for improving scientific and IP searches.

New approaches support sustainability

Application delays and inefficiencies are being resolved through measures that ensure the long-term operational sustainability of patent offices. The European Patent Office (EPO) has sustainability objectives for an engaged staff, digital transformation, an effective patent granting process, and high-impact international cooperation. Other offices are setting similar priorities for enhancing organizational effectiveness.

To provide faster, higher quality examinations, offices are hiring more patent examiners, deploying time-saving technologies such as AI, and transforming their workflows:

• The USPTO hired hundreds of examiners in 2021 to handle growing workloads in keeping with its strategic plan for optimizing patent quality and timeliness. It also is expanding the implementation of AI for patent classifications and searches to give examiners easier access to prior art.
• In anticipation of double-digit application growth in 2021, the UK Intellectual Property Office doubled its examiner headcount in the previous year. Its 5-year program to streamline and modernize processes aims to achieve efficiencies of at least 3.5% of core operating costs. It also is evaluating how to make the best use of AI as it invests more in service delivery.
• The EPO, in a key initiative called “Master the Prior Art,” is improving classification procedures to increase search accuracy and retrieve relevant documents earlier in examinations. It is systematically applying artificial intelligence, machine learning and other technologies to create a more efficient end-to-end, digital patent granting process.

AI-enabled solutions can be used by patent offices for many functions beyond prior art searches. These include classifications assignment and conversion, APIs for more efficient document delivery, online tools for reviews and analysis, and more. By enhancing efficiency, patent quality, and customer service, these solutions can create a multiplier effect for helping offices achieve strategic objectives for operational sustainability and global innovation.

AI and workflow transformations show promise in reducing application delays 

CAS recently completed a project with The National Institute of Industrial Property (INPI) of Brazil that streamlines prior art searches through a unique blend of curated data, artificial intelligence, custom workflow, and outsourced IP search services. The project generated significant benefits:

• Up to 50% reduction in examination times
• 77% of all national applications processed required less examiner search time
• 29% of all national applications processed required little or no additional search
• Productivity gains contributed to a reduction of 80% in the office’s backlog

Learnings for optimizing AI performance in prior art search 

AI is receiving growing attention from patent offices for its ability in quickly analyze millions of data sets and deliver relevant results. According to WIPO, more than 70 AI-related projects are underway in 27 offices, including 19 that focus on prior art searches and examination procedures

Our work with INPI Brazil reinforced several foundational principles for optimizing the application of AI in prior art searching:

• Clean, structured data significantly improves predictive accuracy
• Multiple algorithms are needed to return similarities with the highest relevance
• Augmenting technology with human expertise improves outcomes

Data Quality: Most publicly available non-curated scientific and patent data present inherent challenges for patent offices. They often include transcription errors, mislabeled units, and overly complex patent language. Foreign languages present special challenges.

Human-curated data that has been normalized, prepared, and connected in a structured format improves the training of AI algorithms and increases the performance of prior art searches. Curated data reveal more patents with similarities and identify adjacent patents that could raise obviousness concerns.

Multiple Algorithms: The use of multiple algorithms customized for specific search methodologies improves AI performance. In Brazil, we developed 10 algorithms to deliver the first round of results. Another algorithm for ensemble learning analyzed those findings to produce a final set of highly relevant, ranked results. 

Optimized Workflows:  Our custom workflow solution for INPI Brazil cut the number of prior art search steps in half. It also saves time by making patent and non-patent search results, references, and tools for sorting, filtering, and visualization available through a single cloud-based interface. Custom workflows can be fully automated or include outside search experts who can validate algorithm models and filter searches to augment examiner capacity. 

Interested in learning more about AI-enabled workflow approaches in patent offices and how predictive technology can help ensure the sustainability of the entire intellectual property ecosystem? Read our CAS Insights Report - "Addressing Sustainability of the Global Patent System: The Role of AI in Enhancing Productivity

A critical step in the race to develop treatments for COVID-19 is for scientists to gain a clear understanding of exactly how the virus enters our cells. This insight will support development of targeted anitviral treatments focused on blocking that pathway.

Research on the first SARS-CoV virus, which emerged in 2002 causing an epidemic, as well as on SARS-CoV-2, the related coronavirus that is now causing COVID-19, shows that in both cases a spike (S) protein that protrudes from the viral membrane binds to at least one protein, angiotensin-converting enzyme 2 (ACE2), on the surface of human cells. After binding, proteases, which are human enzymes that clip other proteins into pieces, cut, or “prime”, the spike protein to remove its outer segment, named S1, and reveal the inner segment, named S2. The spike protein S2 segment then causes fusion of the viral membrane with the human cell membranes, letting the viral genetic material enter the cell and start replicating. A recent post summarized this process highlighting the role of ACE2. In this post, I’ll go into more detail about the role that human proteases play in assisting the virus in entering our cells and highlight antiviral treatments targeting that interaction. 

The SARS-CoV-2 spike protein: A tale of two segments

The SARS-CoV-2 spike protein is shaped somewhat like a screw with a larger head and a longer, thinner stalk (Figure 1). Three spike proteins bind to each other to form a trimer, which is shaped, predictably, like a bigger screw. The stalk is inserted into the viral membrane and holds the head outwards away from the virus. The larger head region and part of the stalk are called the S1 region of the spike protein. The remaining part of the stalk that’s closer to the viral membrane is called the S2 region.

Once it enters the body and comes into contact with respiratory system, gastrointestinal tract, blood vessel or other cells that express ACE2 on their surfaces, the spike protein’s S1 region binds to ACE2 on the cell surface and tethers the virus to the outside of the human cell. This is the first step in the viral replication process.

SARS-CoV-2 enters cells one way or another

Once the virus has bound itself to the cell, it has two different potential pathways for entry (Figure 2). Which pathway is used depends on whether or not human proteases are present to “prime” the spike protein. The presence of proteases depends on the type of human cell that the virus is entering and on the particular conditions at that cell. Several human proteases can cleave the spike protein, including transmembrane serine proteinase 2 (TMPRSS2), furin, elastase and trypsin. TMPRSS2 is expressed by human lung cells. Thus, it is thought that it plays an important part in virus entry into respiratory system cells. 

If these proteases happen to be present near the spike-ACE2 binding interface, they will cleave the spike protein to expose the S2 region, and specifically the fusion peptide region, of the spike protein. This fusion peptide region of spike is made of more hydrophobic, or lipid-like, amino acids, and it inserts into the lipid-containing cell membrane to induce viral membrane ̶ cell membrane fusion and subsequent entry of the viral genome into the cell (Figure 2a). This cleavage must occur after spike-ACE2 binding. If it occurs before, the virus is less able to infect the cell. 

If proteases don’t happen to be present near the spike ̶ ACE2 binding interface, the virus will enter the cell by a different pathway called endocytosis (Figure 2b). In this process, the coronaviruses bound to ACE2 proteins outside the cell are engulfed by an indentation in a small region of the cell’s membrane, which then pinches off to form an endocytic vesicle that brings the outside material into the cell. After this happens, the endocytic vesicle fuses with an intracellular membrane-walled vesicle called an endosome. In the endosome, there are proteases present, including one called cathepsin L, that can cleave the spike protein and expose its fusion peptide region. The fusion peptide then mediates fusion of the viral membrane with the endosome’s membrane and thereby induces subsequent entry of the viral genome into the cell.

Recent evidence suggests that there may be a third way that SARS-CoV-2 can enter cells. When the virus is replicating and making new virus particles inside cells, some of the spike proteins might be pre-cleaved, or pre-primed, by a human protease called furin during the new virus assembly process. This means that once the virus breaks out of the cell, those viruses with pre-primed spike proteins can fuse with and infect other cells even if those other cells have low levels of proteases present for one of the two “normal” spike protein cleavage pathways described above.

Planning a counter-attack

Researchers are working hard on drugs that can target the spike ̶ ACE2 ̶ membrane fusion ̶ endocytosis part of the infection lifecycle to hinder COVID-19. Our previous post highlighted recombinant soluble ACE2 as a potential treatment. It works by inactivating the spike protein before SARS-CoV-2 can bind to ACE2 on the surface of cells. However, many other drug candidates are under consideration as well.

Nafomastat and MI-1851 inhibit the proteases involved in spike protein cleavage, TMPRSS2 and furin, respectively, showing potential to reduce SARS-CoV-2 infection in the test tube. Peptides, which are very short proteins that are similar to small regions of the spike protein, have been shown to inhibit fusion of the viral and human cell membranes by “clogging up” the primed spike protein on the virus as it’s changing shape during the membrane fusion process. This prevents viral entry. Finally, PIKfyve inhibitors are known blockers of SARS-CoV-2 infection. PIKfyve is a human lipid kinase, which is an enzyme that adds a phosphate group to specific lipids. As PIKfyve is involved in endosomal metabolism in the endocytic pathway of viral entry, PIKfyve inhibitors have antiviral activity. 

These are just a few of the many drug candidates being studied as SARS-CoV-2 viral entry inhibitors. However, there are many targets available to researchers seeking treatments for COVID-19. The spike protein, ACE2, the proteases that cleave the spike protein and components of the endocytic pathway are all possibilities being studied, and there are many substances that have antiviral activity relative to each of these targets. To help scientists identify some of those potential candidates faster, CAS has released an open source dataset assembled from CAS REGISTRY^® that includes known anti-viral drugs and related chemical compounds that are structurally similar to known antivirals. Learn more and download it and other CAS open access COVID-19 resources here.