Artificial Intelligence
AI in Drug Discovery Market is Expected to Reach $4.0 billion | MarketsandMarkets.
Chicago, May 29, 2023 (GLOBE NEWSWIRE) — AI (Artificial Intelligence) is poised to revolutionize the drug discovery industry in the near future, offering immense potential to accelerate and enhance the process of developing new pharmaceuticals. With its ability to analyze vast amounts of data, identify patterns, and generate insights, AI has already proven to be a valuable tool in various stages of drug discovery, including target identification, lead optimization, and clinical trial design. By leveraging machine learning algorithms, AI can efficiently screen large libraries of compounds, predict their properties, and identify potential drug candidates with higher accuracy and speed than traditional methods. Additionally, AI-driven approaches can aid in the repurposing of existing drugs for new indications, reducing development time and costs. The integration of AI technologies with advanced robotics and high-throughput screening platforms further enhances the efficiency of drug discovery pipelines. Moreover, AI enables the exploration of complex biological data, including genomics, proteomics, and clinical data, facilitating the identification of biomarkers, patient stratification, and personalized medicine approaches. While challenges such as data quality, regulatory considerations, and ethical implications need to be addressed, the transformative potential of AI in drug discovery holds great promise for the future of pharmaceutical research and development.
AI in Drug Discovery market in terms of revenue was estimated to be worth $0.6 billion in 2022 and is poised to reach $4.0 billion by 2027, growing at a CAGR of 45.7% from 2022 to 2027 according to a latest report published by MarketsandMarkets™. The growing need to curb the drug discovery cost & reduce the overall time taken in this process, the rising adoption of cloud-based applications and services, and the impending patent expiry of blockbuster drugs are some of the key factors driving the growth of this market.
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AI in Drug Discovery Market Scope:
| Report Coverage | Details |
| Market Revenue in 2022 | $0.6 billion |
| Estimated Value by 2027 | $4.0 billion |
| Growth Rate | Poised to grow at a CAGR of 45.7% |
| Market Size Available for | 2020-2027 |
| Forecast Period | 2022–2027 |
| Forecast Units | Value (USD Billion) |
| Report Coverage | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
| Segments Covered | Offering, Technology, Application, End User, And Region |
| Geographies Covered | North America (US, and Canada), Europe (Germany, France, UK, Italy, and the RoE), Asia Pacific (Japan, China, India, and RoAPAC), and RoW |
| Report Highlights | Updated financial information / product portfolio of players |
| Key Market Opportunities | Growing biotechnology industry |
| Key Market Drivers | Growing number of cross-industry collaborations and partnerships |
Notable AI in Drug Discovery mergers and acquisitions for 2022 – 2022 include:
- In March 2021, Iktos (France) collaborated with Pfizer (US) to apply Iktos’ AI-driven de novo design software to selected small-molecule programs of Pfizer
- In October 2020, Genesis Therapeutics (US) partnered with Genentech (US) for a multi-target drug development deal using Genesis’ graph machine learning capabilities to identify drug candidates for a range of disorders.
- In February 2021, Exscientia (UK) and the University of Oxford collaborated to develop treatments for Alzheimer’s disease
Growth Drivers of AI in Drug Discovery Market from macro to micro:
Macro-Level Drivers:
- Increasing Demand for Effective and Safe Therapeutics: The rising prevalence of complex diseases and the need for innovative and personalized treatment options drive the demand for more efficient and effective drug discovery processes. AI offers the potential to accelerate drug discovery, improve target identification, and optimize drug development, leading to the discovery of novel and safer therapeutics.
- Technological Advancements: Advancements in AI technologies, such as machine learning, deep learning, and natural language processing, have significantly improved the capabilities of AI in analyzing and interpreting complex biological data. These advancements enable the integration of diverse data sources, such as genomics, proteomics, and clinical data, leading to more comprehensive insights and faster decision-making in drug discovery.
- Availability of Big Data: The exponential growth of biological data, including genomic sequences, protein structures, and drug-target interactions, provides a rich source for AI-driven analysis and modeling. The availability of big data sets enables AI algorithms to identify patterns, predict compound properties, and generate novel hypotheses, facilitating more informed and data-driven decision-making in drug discovery.
Micro-Level Drivers:
- Cost and Time Efficiency: Traditional drug discovery processes are time-consuming, expensive, and prone to failure. AI-driven approaches offer the potential to reduce costs and time by streamlining various stages of drug discovery, such as compound screening, lead optimization, and clinical trial design. AI algorithms can quickly analyze vast libraries of compounds, prioritize candidates, and predict their properties, leading to faster and more efficient drug development.
- Target Identification and Validation: AI can aid in the identification and validation of potential drug targets by integrating and analyzing diverse data sources, including genomic, proteomic, and clinical data. By identifying novel targets and elucidating their biological mechanisms, AI enables researchers to develop targeted therapies with higher efficacy and specificity.
- Drug Repurposing and Combination Therapy: AI algorithms can analyze large databases of existing drugs and their known interactions to identify opportunities for drug repurposing. By repurposing existing drugs for new indications or exploring drug combinations, AI can accelerate the development of new treatment options and increase the success rate of clinical trials.
- Personalized Medicine and Precision Healthcare: AI-driven approaches facilitate the integration of patient data, including genetic profiles and clinical parameters, to develop personalized treatment strategies. By identifying patient subgroups and predicting individual responses to therapies, AI enables precision medicine approaches that optimize treatment outcomes and minimize adverse effects.
These macro and micro-level drivers collectively contribute to the growth of the AI in drug discovery market, fostering innovation, improving efficiency, and advancing the development of safe and effective therapeutics.
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Hypothetic challenges of AI in Drug Discovery market in near future:
- Data Quality and Availability: AI relies heavily on high-quality, diverse, and comprehensive data for training and model development. However, ensuring the availability of reliable and well-curated data sets can be a challenge, especially in the drug discovery field where data privacy, intellectual property, and regulatory considerations are significant. Obtaining access to large and representative data sets that encompass diverse patient populations and disease types may be a hurdle for AI-driven drug discovery efforts.
- Interpretability and Explainability: AI algorithms, particularly deep learning models, often operate as “black boxes,” making it challenging to interpret the reasoning behind their predictions or recommendations. In drug discovery, where decision-making needs to be transparent and explainable, the lack of interpretability may raise concerns among regulators, clinicians, and patients. Addressing the interpretability challenge of AI models is crucial to build trust and acceptance in the field.
- Validation and Regulatory Compliance: Validating AI-driven models and ensuring compliance with regulatory standards present challenges in the drug discovery industry. Regulators typically require a high level of evidence and validation to ensure the safety and efficacy of new drugs. AI models must meet rigorous standards and demonstrate robust performance on diverse datasets to gain regulatory approval. Developing a regulatory framework that adequately addresses the unique considerations of AI in drug discovery is essential to enable its wider adoption.
- Ethical Considerations: The use of AI in drug discovery raises ethical concerns, such as the potential bias in algorithmic decision-making, privacy and security of patient data, and the impact on employment in the pharmaceutical industry. Ensuring fairness, transparency, and accountability in AI systems is essential to mitigate these ethical challenges and ensure that AI-driven drug discovery benefits all stakeholders.
- Human-AI Collaboration: While AI can enhance and accelerate drug discovery processes, it is not a substitute for human expertise. Effective collaboration between AI systems and human researchers is crucial to leverage the strengths of both. Integrating AI into existing workflows, addressing the challenges of data integration, and fostering a culture that encourages collaboration between AI experts and domain experts are essential to maximize the potential of AI in drug discovery.
Addressing these hypothetical challenges will be critical to harness the full potential of AI in the drug discovery market. Collaboration among stakeholders, regulatory frameworks that adapt to AI technologies, and ongoing research and development efforts focused on data quality, interpretability, validation, and ethical considerations will help overcome these challenges and drive the responsible and effective use of AI in drug discovery.
Top 3 use cases of AI in Drug Discovery market:
- Drug Discovery and Design: AI can significantly accelerate the process of drug discovery by efficiently screening vast libraries of compounds and predicting their properties. AI algorithms can analyze large amounts of data, including chemical structures, biological activity, and toxicity profiles, to identify potential drug candidates with higher accuracy and speed than traditional methods. This use case allows researchers to explore a wider range of possibilities and prioritize the most promising candidates for further development, ultimately reducing the time and cost required to bring new drugs to market.
- Target Identification and Validation: Identifying and validating suitable drug targets is a critical step in the drug discovery process. AI algorithms can analyze complex biological data, such as genomics, proteomics, and clinical data, to identify potential targets and elucidate their biological mechanisms. By integrating diverse data sources and leveraging machine learning techniques, AI can uncover novel drug targets, validate their relevance to specific diseases, and predict the likelihood of success in drug development. This use case enables researchers to focus their efforts on targets with a higher probability of therapeutic success.
- Personalized Medicine and Precision Healthcare: AI has the potential to revolutionize personalized medicine by integrating patient data, such as genetic information, clinical parameters, and lifestyle factors. AI algorithms can analyze this data to identify patient subgroups, predict individual responses to therapies, and optimize treatment strategies. By tailoring treatments to individual patients based on their unique characteristics, AI enables precision healthcare approaches that improve treatment outcomes, minimize adverse effects, and optimize patient care. This use case has the potential to transform the way diseases are diagnosed, monitored, and treated, leading to more effective and personalized therapeutic interventions.
These top three use cases of AI in drug discovery highlight the transformative potential of AI technologies in accelerating the discovery of new drugs, improving target identification and validation, and enabling personalized medicine approaches. By leveraging AI’s capabilities, researchers and pharmaceutical companies can enhance their understanding of diseases, optimize drug development processes, and ultimately deliver safer and more effective treatments to patients.
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Artificial Intelligence
Converge Technology Solutions Announces Deconsolidation of Portage CyberTech Inc.
TORONTO and GATINEAU, QC, July 3, 2024 /PRNewswire/ — Converge Technology Solutions Corp. (“Converge” or the “Company”) (TSX: CTS) (FSE: 0ZB) (OTCQX: CTSDF) today announced that, as of June 27, 2024, the Company has fulfilled the criteria necessary for the deconsolidation of its majority owned subsidiary, Portage CyberTech Inc. (“Portage CyberTech”) for accounting purposes, following Converge’s decision to relinquish its right to majority representation on the board of directors of Portage CyberTech (the “Portage Board”) pursuant to a voting agreement dated as of June 27, 2024 (“Voting Agreement”).
In conjunction with the execution of the Voting Agreement, Portage CyberTech announced it has entered into a new stand-alone credit facility with the Canadian Imperial Bank of Commerce (“CIBC”), for up to $15 million, of which $10 million will be drawn immediately and the additional $5 million is contingent on achieving future financial targets.
“Successfully meeting the criteria necessary to deconsolidate Portage CyberTech from Converge is an important step for each company,” stated Shaun Maine, Group CEO of Converge. “This pivot will allow Converge to remain a strong partner and advocate for Portage CyberTech’s industry leading products and positions Portage CyberTech on its own accelerated growth path, operating completely independent of Converge.”
Converge Group CEO, Shaun Maine will maintain the position as Chairman of the Portage Board, which consists of three members. Converge currently retains ownership of approximately 51% of the outstanding common shares of Portage CyberTech in addition to the $25 million long-term loan entered into with Portage CyberTech.
About Portage CyberTech, A Converge Company
Portage CyberTech powers trusted digital transactions between individuals, businesses, and government organizations. Driven by some of the most ambitious digital projects and our desire to raise the visibility of our clients at home and abroad, our committed team of experts in all things digital – identity, access management, trusted services, and communications, have created the solutions designed to reach your customers. For more information, visit portagecybertech.com.
About Converge
Converge Technology Solutions Corp. is a services-led, software-enabled, IT & Cloud Solutions provider focused on delivering industry-leading solutions. Converge’s global approach delivers advanced analytics, artificial intelligence (AI), application modernization, cloud platforms, cybersecurity, digital infrastructure, and digital workplace offerings to clients across various industries. The Company supports these solutions with advisory, implementation, and managed services expertise across all major IT vendors in the marketplace. This multi-faceted approach enables Converge to address the unique business and technology requirements for all clients in the public and private sectors. For more information, visit convergetp.com.
Forward-Looking Information
This press release contains certain “forward-looking information” and “forward-looking statements” (collectively, “forward-looking statements”) within the meaning of applicable Canadian securities legislation regarding Converge, Portage Cybertech and their businesses. Any statement that involves discussions with respect to predictions, expectations, beliefs, plans, projections, objectives, assumptions, future events or performance (often but not always using phrases such as “expects”, or “does not expect”, “is expected” “anticipates” or “does not anticipate”, “plans”, “budget”, “scheduled”, “forecasts”. “estimates”, “believes” or “intends” or variations of such words and phrases or stating that certain actions, events or results “may” or “could”, “would”, “might” or “will” be taken to occur or be achieved) are not statements of historical fact and may be forward-looking statements.
Although the Company bases these forward-looking statements on assumptions that it believes are reasonable when made, the Company cautions investors that forward-looking statements are not guarantees of future performance and that its or Portage Cybertech’s actual results of operations, financial condition and position in the industry in which they each operate may differ materially from those made in or suggested by the forward-looking statements contained in this press release. In addition, even if the Company or Portage Cybertech’s results of operations, financial condition and position in the industry in which they each operate are consistent with the forward-looking statements contained in this press release, those results of developments may not be indicative of results or developments in subsequent periods.
There can be no assurance that such information will prove to be accurate, as actual results and future events could differ materially from those anticipated in such information. No forward-looking statement is a guarantee of future results. Accordingly, you should not place undue reliance on forward-looking information, which speaks only as of the date made. The forward-looking information contained in this press release represents the Company’s expectations as of the date specified herein, and are subject to change after such date. However, the Company disclaims any intention or obligation or undertaking to update or revise any forward-looking information or to publicly announce the results of any revisions to any of those statements, whether as a result of new information, future events or otherwise, except as required under applicable securities laws.
All of the forward-looking information contained in this press release is expressly qualified by the foregoing cautionary statements.
For further information contact: Converge Technology Solutions Corp., Email: [email protected], Phone: 416-360-1495
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Artificial Intelligence
Trianz Welcomes Priyanshu Singh as Vice President & Chief of Staff to the CEO
SANTA CLARA, Calif., July 3, 2024 /PRNewswire/ — Trianz, a global digital transformation technology solutions and services firm, is thrilled to announce the appointment of Priyanshu Singh as its new VP & Chief of Staff. This strategic hire comes at a pivotal moment as Trianz continues to experience remarkable growth, driven by its shift to an ‘IP-led digital transformation’ model with hyper-automated platforms like Concierto – the hybrid cloud migrations and operations platform, and Extrica – the data to AI platform, accelerating customer success.
As Chief of Staff, Priyanshu Singh will play a crucial role in leading Trianz’ IP-led strategy development, operational excellence, and key execution teams, supporting the CEO’s office. He will design and implement organization-wide processes and capabilities and create and oversee opearational monitoring models and enterprise analytics. His expertise in business and competitive strategy, customer experience, digital transformation, and operations across multiple industries will be a tremendous asset as Trainz prepares to scale in this new model.
A graduate of the prestigious National Defense Academy, Priyanshu began his career in the Indian Army. After completing his MBA from IIM Calcutta, Priyanshu joined McKinsey & Company, working on strategy and business model design for clients across industries. His distinguished career includes key leadership roles at Uber, Lithium Urban Technologies, The Adecco Group, and Honeywell. Known for his excellence in reimagining offerings, exceeding support targets, and spearheading innovative solutions, Priyanshu has consistently delivered exceptional results. In 2016, he was recognized as one of India’s ‘Top 40 Under 40′ business leaders by The Economic Times and Spencer Stuart.
“We are excited to welcome Priyanshu Singh to our leadership team. With his military background, he brings a unique blend of discipline and strategic insight to Trianz,” said Sri Manchala, CEO of Trianz. “His deep expertise and forward-thinking approach will be invaluable in shaping our vision as we continue to leverage the power of our in-house innovations, AI, and cutting-edge tools. With Priyanshu on board, I am confident we will accelerate our transformations and achieve operational excellence.”
“I am honored to join Trianz as the new Chief of Staff,” said Priyanshu. “In this role, I will focus on bridging the gap between strategy and execution, ensuring our teams are aligned to achieve our committed objectives. I am passionate about problem-solving and look forward to collaborating with leaders across the organization to foster a culture of agility, adaptability, and continuous improvement. I am thrilled to be part of this dynamic team and confident we can achieve great things together.”
Priyanshu’s appointment underscores Trianz’ commitment to strengthening its leadership team and ensuring the company is well-prepared to seize future opportunities.
About Trianz
Trianz is a leading-edge technology platforms and services company that accelerates digital transformations at Fortune 100 and emerging companies worldwide in data & analytics, digital experiences, cloud infrastructure, and security. Our “IP Led Transformations” approach, informed by insights from a recent global study spanning 20+ industries and 5000+ companies, addresses challenges posed by the rapid pace of AI-driven transformation, digital talent scarcity, and economic uncertainty. Our IP and platforms, including Concierto , Extrica , and Pulse, revolutionize cloud adoption, data analytics, and AI insights, empowering organizations to navigate the complexities of digital transformation seamlessly.
Founded in California and with an organization of over 2,000 associates across the United States and India, Trianz is a Premier Partner of AWS, consistently rated #1 by clients for value delivery over the past five years. Trianz has been ranked as one of the best Consulting Firms by Forbes and has been certified as a Great Place to Work for three years in a row. To learn more about Trianz, email [email protected] or visit www.trianz.com.
Watch Trianz CEO Sri Manchala’s insightful interview with Bloomberg on Partner | Crossing The Digital Faultline & Leading Towards Transformative Success – YouTube and delve deeper into his book Crossing the Digital Faultline at Crossing the Digital Faultline | Trianz.
Trianz Media Team [email protected]+1-408-387-5800
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Artificial Intelligence
Internet of Things (IoT) in Energy Market to Reach USD 68.2 Billion by 2031 Owing to Rapid Growth in Digitization of Energy Industry | SkyQuest Technology
WESTFORD, Mass., July 3, 2024 /PRNewswire/ — According to SkyQuest, the global Internet of Things (IoT) in Energy Market size was valued at USD 25.2 billion in 2022 and is poised to grow from USD 28.1 billion in 2023 to USD 68.2 billion by 2031, growing at a CAGR of 11.7% in the forecast period (2024-2031).
Growing use of automation in the energy industry is forecasted to bolster the demand for internet of things (IoT) in energy over the coming years. Deployment of smart meters and rising demand for smart grid integration are also predicted to create new opportunities for internet of things (IoT) in energy providers in the future. The global internet of things (IoT) in energy market is segmented into component, network technology, organization size, application, and region.
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Internet Of Things (IoT) In Energy Market Overview:
Report Coverage
Details
Market Revenue in 2023
$ 28.1 Billion
Estimated Value by 2031
$ 68.2 Billion
Growth Rate
Poised to grow at a CAGR of 11.7%
Forecast Period
2024–2031
Forecast Units
Value (USD Billion)
Report Coverage
Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
Segments Covered
Component, Application and Technology
Geographies Covered
North America, Europe, Asia Pacific, Middle East & Africa, Latin America
Report Highlights
High use of smart meters in the energy sector
Key Market Opportunities
Development of satellite networks to improve IoT connectivity for energy applications
Key Market Drivers
Grid modernization is estimated to bolster the demand for internet of things (IoT) in energy
Segments covered in Internet of Things (IoT) In Energy Market are as follows:
ComponentPlatform, Solutions and ServicesApplicationOil & Gas, Smart Grid, and Cool MiningTechnologyCellular Network, Satellite Network, Radio Network and OthersRequest Free Customization of this report:https://www.skyquestt.com/speak-with-analyst/internet-of-things-in-energy-market
Most Internet of Things (IoT) Devices in Energy Sector Connect through Cellular Network
Most IoT devices and components can be connected using a cellular network as cellular infrastructure is already established. Cellular networks offer connectivity for mobile IoT devices and systems used in the energy sector. Growing demand for better connectivity for automation of the energy industry is projected to help this segment account for a substantial share of the global internet of things in the energy market. The advent of 5G technology is also estimated to create new opportunities for internet of things in energy providers in this segment going forward.
An increasing number of IoT and telecommunication network providers are focusing on developing satellite constellations, especially for IoT devices. Low earth orbit (LEO) satellites are being extensively used to establish new communication networks for IoT in energy and other applications. High demand for resilient and reliable connectivity will also help this segment generate new business opportunities in the future.
Smart Grid Applications Lead Internet of Things (IoT) in Energy Adoption Owing to High Investments in Grid Modernization
Smart technologies are being integrated into multiple energy applications to satisfy surging demand for energy and efficient energy management as well. The use of smart meters and the high demand for automation in modern energy grids will also create new opportunities for internet of things in energy companies in the future. High demand for predictive maintenance capabilities in smart grids will also create new opportunities for internet of things (IoT) in energy market players going forward.
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Platforms Remain Essential for IoT Adoption in Energy Industry
Platforms play a crucial role in the functional operation of IoT devices and systems, which is why this segment is projected to account for a dominant market share. Device connectivity is also projected to be highly dependent on platforms, which is why all internet of things (IoT) in energy companies should focus on this segment to bolster market growth in the long run.
Increasing digitization of the energy industry is expected to create several new opportunities for internet of things (IoT) in energy providers going forward. From start-ups to leading internet of things (IoT) in energy companies all have an equal chance of maximizing their business scope in the future.
Related Reports:
Internet Of Things (IoT) Market
Narrowband-IoT (NB-IoT) Market
Consumer IoT Market
Internet of Things (IoT) in Smart Cities Market
IoT Security Market
About Us:
SkyQuest is an IP focused Research and Investment Bank and Accelerator of Technology and assets. We provide access to technologies, markets and finance across sectors viz. Life Sciences, CleanTech, AgriTech, NanoTech and Information & Communication Technology.
We work closely with innovators, inventors, innovation seekers, entrepreneurs, companies and investors alike in leveraging external sources of R&D. Moreover, we help them in optimizing the economic potential of their intellectual assets. Our experiences with innovation management and commercialization has expanded our reach across North America, Europe, ASEAN and Asia Pacific.
Contact:Mr. Jagraj SinghSkyquest Technology1 Apache Way,Westford,Massachusetts 01886USA (+1) 351-333-4748Email: [email protected] Our Website: https://www.skyquestt.com/
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