Biotech AI
Why AI?)
The video explains how IBM helps its clients understand the value AI can deliver to their businesses. It outlines a framework for evaluating potential AI projects based on two axes: feasibility (how easy or hard it is to implement) and value (the benefits it provides). Projects are categorized as “no-brainers” (high feasibility, high value), “low-hanging fruit” (high feasibility, low value), “big bets” (low feasibility, high value), or backlogged (low feasibility, low value).
The video emphasizes that the real value of AI lies in its ability to generate value for businesses in three key areas:
1. People and operational efficiency: AI can save time and effort by automating tasks currently done by employees.
2. Risk reduction: AI can help mitigate various types of risks, including legal, financial, and operational risks.
3. Revenue generation and cost savings: AI can enable new revenue opportunities or reduce costs by automating processes that were previously too time-consuming or expensive to pursue.
The video provides a framework for quantifying the potential value of AI projects by considering factors such as the number of people involved, the percentage of their time spent on a task, average salaries, potential risks averted, and new revenue or cost savings opportunities.
As language models like ChatGPT and Gemini have ushered in a new age of AI in Silicon Valley, the world’s most powerful tech companies are looking ahead to drug discovery and digital biology.
The document discusses how recent advances in artificial intelligence, particularly in deep learning and generative AI models, are providing profound breakthroughs in biomedical research and drug discovery. The key points are:
1. AlphaFold, a deep learning model developed by Google DeepMind, has made remarkable progress in predicting the 3D structures of proteins, a longstanding challenge in structural biology.
2. AI models are being trained on vast amounts of biomedical data (gene sequences, imaging data, patient records, etc.) to discover patterns and generate new hypotheses about disease mechanisms, drug targets, and potential drug compounds.
3. Major pharmaceutical companies and startups are investing heavily in developing AI-driven drug discovery platforms, using techniques like federated learning to address data privacy concerns.
4. AI is being used in various stages of drug development, from identifying targets and designing novel compounds to predicting drug efficacy, side effects, and patient responses.
5. Early results suggest AI could significantly accelerate and improve the productivity of drug development, which has historically been a slow and costly process with high failure rates.
6. While promising, the true impact of AI on drug discovery remains to be seen, but it has the potential to decipher biology in new ways and “drug the undruggable” if successful.
In summary, AI is seen as a transformative force in biomedicine, with the ability to generate insights and hypotheses that human researchers may have missed, potentially revolutionizing the way we understand and treat diseases.
The rise of pan-resistant bacteria is creating an urgent need for structurally novel antibiotics. Artificial intelligence methods can discover new antibiotics, but existing methods have notable limitations. Property prediction models, which evaluate molecules one-by-one for a given property, scale poorly to large chemical spaces. Generative models, which directly design molecules, rapidly explore vast chemical spaces but generate molecules that are challenging to synthesize. Here we introduce SyntheMol, a generative model that designs new compounds, which are easy to synthesize, from a chemical space of nearly 30 billion molecules. We apply SyntheMol to design molecules that inhibit the growth of Acinetobacter baumannii, a burdensome Gram-negative bacterial pathogen. We synthesize 58 generated molecules and experimentally validate them, with six structurally novel molecules demonstrating antibacterial activity against A. baumannii and several other phylogenetically diverse bacterial pathogens. This demonstrates the potential of generative artificial intelligence to design structurally novel, synthesizable and effective small-molecule antibiotic candidates from vast chemical spaces, with empirical validation.
Junction Bioscience is building an autonomous AI scientist to navigate the discovery of transformative medicines. Our scientific hypothesis engine iterates upon breakthrough chemistry from the laboratory to achieve clarity and control over the molecular basis of disease. We focus on the intersection of neuroinflammation and immunology where uncommon molecular insights position us to develop best-in-class therapies for millions of patients in need.
Junction Bioscience is a therapeutics company developing an autonomous AI system to aid in the discovery of new medicines. Their approach involves an AI “hypothesis engine” that can rapidly iterate and refine ideas to identify promising therapeutic targets and mechanisms. The company focuses on the intersection of neuroinflammation and immunology, leveraging its AI capabilities to gain insights into the molecular basis of disease and design potential treatments.
Key points:
1. Junction Bioscience is building an AI system called a “hypothesis engine” to formulate, test, and refine ideas for drug discovery.
2. The company aims to reconcile biophysics with pathway biology to gain clarity into disease mechanisms and identify targets for intervention.
3. Their technology can dramatically improve the signal-to-noise ratio in measuring molecular interactions in living cells.
4. The company is focused on autoimmune and inflammatory diseases, leveraging its AI capabilities to develop best-in-class therapies.
5. The team is led by Brian Petkov, a theoretical physicist and drug hunter, and they have received early partnerships and collaborations with academic institutions and pharmaceutical companies.
In summary, Junction Bioscience is using cutting-edge AI and wet-lab techniques to overcome bottlenecks in drug discovery and develop transformative medicines for patients with unmet needs.
At Diffuse Bio we’re building a push-button entirely AI software platform for molecular design, leveraging breakthroughs in generative AI. Our team has been behind breakthroughs in AI protein design for the past 6 years, including the first experimental validation of AI-generated proteins and diffusion models for protein structure and sequence.
Here is a summary of the key points:
- Namrata is the founder of Diffuse Bio, a company that uses computational methods and AI to design therapeutics, vaccines, and enzymes in a better, faster, and cheaper way than existing methods.
- The problem they are addressing is that while computational protein design methods have had some success, they suffer from low success rates in experimental validation and are slow and inefficient.
- Diffuse Bio has developed new AI-based approaches, including the first diffusion models for protein structure and sequence generation. Their models can produce novel protein structures starting from just noise.
- They are scaling up these AI methods to tackle grand challenges in molecular design across tasks like loop redesign, sequence engineering, binder design, and de novo structure generation.
- The company’s work has been covered in major media outlets like the New York Times and NBC News.
- They are seeking to connect with companies working on protein therapeutics, antigen/enzyme design, and high-throughput protein characterization assays.
Nanograb is a computational drug discovery company that uses AI to generate the best combination of binders to treat different diseases. Our product allows drugs to be targeted to very specific areas of the body.
Nanograb’s vision is to transform the therapeutic landscape across the entire healthcare spectrum. Nanograb will be creating new therapies on-demand with next-generation targeting whether it’s infinitely mutating cancers, incurable neurodegenerative diseases or Disease X
Olio labs uses AI to develop combination therapeutics that consider the thousands of interacting proteins in your body rather than targeting just one or two. Their lead combinations target obesity and are more effective with fewer side effects than Ozempic, the fastest growing drug of all time. Their custom ML built from real-world expertise and cutting edge AI searches trillions of combinations to find the perfect one.
Arpeggio Bio is a pioneering pharmaceutical company that develops drugs targeting transcription factors using AI and high-throughput RNA-sequencing. With $20M in venture funding, we’ve targeted “undruggable” proteins like NRF2, TEAD, and GPX4 where our lead program is rapidly progressing towards a DC for the treatment of IO-resistant melanoma. With partnerships with J&J and FORMA, we’ve validated our platform in rare disease and inflammation with a significant Phase I success.
Pando is an AI-driven synthetic biology company revolutionizing enzyme engineering for the pharmaceutical industry. Our ultra-high-throughput screening platform screens 1000-fold more enzymes 75% faster and 80% cheaper than traditional methods. This empowers our generative AI to efficiently optimize enzymes across multiple properties, delivering high-performing, tailored enzymes that reduce costs and enhance efficiency.
Argon AI is a platform where biopharma and life sciences professionals can execute complex and data driven workflows using natural language. We help professionals get thorough answers to questions about clinical trials, existing treatments, healthcare landscape, and the competitive market in minutes rather than months.
The problem
Tedious and repetitive workflows: generating key insights for drug development is a highly manual and repetitive task that requires searching across a highly fragmented number of sources, summarizing information, formatting tables/charts, and a number of other tasks. Keeping the output of these workflows up-to-date requires manual data screening and updating.
Existing workflows are not keeping up with demand: at the same time, the amount of healthcare data, the number of drug development programs, and biological complexity is growing exponentially. This only exacerbates the problem, leading to a rise in prices and lower quality and speed.
Argon AI — Solution
We automate the grunt work to free up professionals to focus on strategy and execution! Argon is an AI-first platform that automates the process of aggregating, synthesizing, and generating insights to save teams time and money.
Our products allow teams to quickly find all relevant clinical trials, companies, and therapies for their research. Customers today use Argon for indication selection, competitive intelligence, clinical intelligence, and more.
→ Quickly find relevant clinical trials
We build AI models helping our customers create novel, high performing biomolecules faster. Our AI models simulate the biophysical properties of biomolecules and identify the most promising ones to be tested in the lab.
The multi use bioreactor market value is projected USD 24.58 billion by 2029
Multi Use Bioreactor Market Dynamics
Drivers
Rise in chronic diseases
Chronic diseases are becoming more prevalent, improved technology is being used, biopharmaceutical businesses are using bioreactors, and more research and development projects. These factors are all anticipated to contribute to the multi use bioreactor market’s rapid expansion. On the other hand, over the forecast period, there will be tremendous prospects for the growth of the multi use bioreactor market due to the increased need for personalized medications and the development of orphan drugs.
Research and development activities
The need for clinical-stage bioreactors has also been fuelled by expanding R&D efforts for the worldwide development of preventative vaccines. For the production of the COVID-19 vaccine, Serum Institute of India Pvt. Ltd. (SIIPL) used CSR bioreactors that ABEC donated. As a result, the market will continue to expand significantly over the forecast period to increase bioreactors’ deployment in several production facilities.
Opportunities
Technological advancements in bioreactors
The market will rise in the coming years due to ongoing bioreactor features and processing improvements. Additionally, many firms are actively engaged in developing cutting-edge technology, such as integrated bioreactors, which are cost-effective enough to enable large-scale manufacturing and boost product demand. The simplicity of scaling up volume and big volume processing, increased efficiency, and simplification of in-vivo systems are only a few of the many advantages that advanced bioreactors provide.
Restraints/Challenges
On the other hand, the increasing concern regarding extractable along with limited capacity will obstruct the market’s growth rate. The dearth of skilled professionals and lack of healthcare infrastructure in developing economies will challenge the multi use bioreactor market.
This multi use bioreactor market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the multi use bioreactor market contact Data Bridge Market Research for an Analyst Brief, our team will help you take an informed market decision to achieve market growth.
COVID-19 Impact on Multi Use Bioreactor Market
The sudden increase in COVID-19 viral cases has had a favorable effect on reactor demand. The demand for effective medications for treating diseases and vaccinations for preventing them likewise grows as these needs do. Numerous pharmaceutical and biopharmaceutical companies have increased the capacity of their manufacturing units in order to serve a large patient pool, which has fuelled the need for bioreactors during the epidemic.
Recent Development
In April 2022, Merck announced that it would purchase Lonza’s MAST platform, an automated aseptic bioreactor sampling system. These platforms assist users in optimising data while performing bioprocessing. The company will be able to grow its business in the market thanks to this acquisition
In March 2020, General Electric Life Sciences sold the CYTIVA biopharma division to Danaher Corporation. This tactic has aided the business in expanding its product line for biopharma clients and improving market penetration
Global Multi Use Bioreactor Market Scope
The multi use bioreactor market is segmented on the basis of product type, cell, molecule, modality and technology. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.
Bioreactors are essential tools in biotechnology and industrial microbiology, facilitating the cultivation of various microorganisms, cells, or enzymes in controlled environments. These devices offer precise control over crucial parameters such as temperature, pH, dissolved oxygen, nutrient availability, and agitation, ensuring optimal conditions for the growth and productivity of biological agents.
In terms of technology, bioreactors come in various designs, including stirred-tank reactors, airlift reactors, packed-bed reactors, and membrane reactors, each tailored to specific applications and requirements. Modern bioreactors often incorporate advanced sensors, automation systems, and computerized monitoring and control software, enabling real-time adjustments and optimization of process parameters for enhanced efficiency and productivity.
Economically, bioreactors play a vital role in numerous industries, including pharmaceuticals, biopharmaceuticals, food and beverage, agriculture, environmental remediation, and biofuel production. They enable large-scale production of valuable products such as antibiotics, vaccines, enzymes, biofuels, and recombinant proteins, contributing significantly to revenue generation and economic growth.
Recent trends in bioreactor technology include the development of single-use bioreactors, which offer cost savings, flexibility, and reduced risk of contamination compared to traditional stainless steel reactors. Additionally, there is a growing emphasis on bioprocess intensification, aiming to maximize productivity and minimize resource consumption through innovative reactor designs, optimization strategies, and integration of downstream processing steps.
Moreover, the integration of bioreactors with bioprocess analytics, data analytics, and artificial intelligence (AI) technologies is becoming increasingly prevalent, enabling predictive modeling, process optimization, and decision-making based on real-time data analysis. This trend towards digitalization and smart biomanufacturing holds immense potential for improving process efficiency, product quality, and regulatory compliance while reducing time-to-market and production costs.
Overall, bioreactors continue to evolve as indispensable tools in biotechnology and industrial microbiology, driving innovation, economic growth, and sustainability across various sectors through technological advancements, economic viability, and emerging trends towards digitalization and process intensification.
High-throughput experimentation systems advanced (online) sensor technologies, high-resolution product analytics, and advanced and automated data analytics are the basis for next-generation bioprocess research and development in the Pharmaceutical industry. The need for an end-to-end data infrastructure is often neglected but fundamental in order to unleash the full potential of the generated data.
This case study showcases the digital evolution of our laboratories in Bioprocess Research at Roche Pharma Research and Early Development (pRED), starting with an ELN (Electronic Laboratory Notebook) based approach in the center of the IT landscape. In the last decade laboratory, automation and high-throughput experimentation progressed dramatically, to the point where data collection and efficient storage were becoming the bottleneck in the process. We introduced some major changes to the IT architecture to fit the new data types and to enable the collection of high amounts of well-contextualized data to meet data analytics needs. Our new IT landscape is a microservice-based architecture, which allows us to reuse and build upon key functional systems. At the core sits an in-house developed experiment management tool (Experiment Manager) as a new user-centric platform for bioprocess data management, with the capability of executing workflow orchestration routines for laboratory automation.
By eliminating major technical debts from legacy systems and setting up the basis for a new infrastructure we enabled the easy implementation of laboratory and data automation routines as well as providing analytical dashboards and programmatic data access for cross-functional and cross-process scale data analytics, modeling, and data science.
Now, 120 years later, new airplanes can be simulated with great precision and confidence — but chromatography process development in the biopharmaceutical industry still relies heavily on trial and error.
We build AI models helping our customers create novel, high performing biomolecules faster. Our AI models simulate the biophysical properties of biomolecules and identify the most promising ones to be tested in the lab.
Flourish predicts and rapidly verifies protein sequences with specific, desirable performance characteristics
Seed Health, which became a profitable startup through the selling of direct-to-consumer microbiome science synbiotic supplements, is investing profits in an AI-powered biology platform.
The goal is to make discoveries on the frontier of probiotic science.
Specifically, medical interventions for metabolic health, brain health, longevity and menopause are being targeted.
