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BenevolentAI utilizes AI to accelerate drug discovery, enhancing scientific innovation and the likelihood of discovering successful drugs.

How BenevolentAI can help you:

Empowers scientists with AI to uncover new insights from data.
Accelerates innovation in drug discovery.
Increases the probability of discovering successful drugs.

Why choose BenevolentAI: Key features

Versatile, scalable, and robust AI-enabled drug discovery platform.
Leverages multi-modal data foundations.
Rich drug pipeline with potentially best-in-class and first-in-class drug candidates.
Customizable SaaS products for better research productivity.

Who should choose BenevolentAI:

Biopharmaceutical companies seeking drug discovery acceleration.
Scientists looking for new insights in biomedical data.
Organizations aiming for collaboration in innovative therapeutics.

About BenevolentAI

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https://www.benevolent.com/

Release Date

March 2024

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BenevolentAI Announces Leadership Team Changes

BenevolentAI, a leading clinical-stage AI drug discovery company, has announced changes to its leadership team. Baroness Joanna Shields, CEO of BenevolentAI, will be stepping down from her role and Dr. Ivan Griffin, current COO, will assume the role of Interim CEO.

2 Sources

Wed, 3 Jul, 8:02 AM UTC

Meet Briefly Bio, The British Startup Aiming To Get Scientists Sharing

Scientific advances are built on collaboration: when breakthroughs are achieved, those responsible almost always acknowledge they are "standing on the shoulders of giants". Yet with experimentation in fields such as biology and drug development becoming ever more complex, the principle of building on the work that came before is getting harder to turn into practice. Enter Briefly Bio, a British start-up leveraging artificial intelligence (AI) tools in an attempt to solve this problem. The London-based company, founded last year, will today announce it has raised $1.2 million of pre-seed finance as it launches a software product aimed at helping scientists to share their work with greater clarity and consistency. "Our goal is solve the 'reproducibility crisis' in science," explains Harry Rickerby, CEO of the business - and co-founder with Dr Katya Putintseva and Staffan Piledahl. "We are responding to the issue that such a huge amount of published academic research today is not reproducible." Typically, when scientists confront a challenge - to develop a new drug to treat disease, for example - they start by assessing relevant work that has already been published. But while this has the potential to stand as the foundations for their subsequent research, they can't simply rely on its findings; rather, they must reproduce the work for themselves. Often, this proves very difficult. By some estimates, more than 50% of attempts to reproduce experiments now fail, costing the biotechnology industry worldwide some $50 billion in wasted effort and replicated work. "It's not that the original research was wrong," says Rickerby. "Rather, many experiments are so complicated, that unless you know exactly how they were conducted and in what conditions, you can't reproduce them." Briefly Bio's software therefore aims to ensure scientists have much better access to that information. It sets out a common language in which academics and researchers can record their experimentation as it proceeds. And it includes AI that spots gaps or errors in the record and prompts the scientist to respond. The idea is that the end product of the research will be accompanied by a record of how it was completed, presented in a standard format that any other scientist can pick up and use as they seek to reproduce the work. Rickerby likens the idea to the way in which a platform such as Github has enabled software engineers to collaborate by making it much easier to share common coding solutions and build on each other's work. "We think Briefly can do the same for scientists with their experiments," he says. It's early days for the company, though Rickerby says it has already signed up a number of paying customers for its software-as-a-service business model. He sees corporates such as biotechnology and pharmaceutical companies as the target market for efforts to monetise the software - with academics and individual scientists potentially getting access at a reduced price or for nothing. "Through AI, there is an opportunity for huge improvements in the efficiency of scientific discovery, and companies from start-ups to big pharma are investing hundreds of billions of dollars to take advantage," Rickerby adds. "But to realise this potential, science needs more consistency and transparency in how these datasets are generated." Early adopters of the software are impressed by its applications. For example, Dr Maria Anastasina, Wet Lab Head at the Evolutionary and Synthetic Biology Unit of the Okinawa Institute of Science and Technology says: "Briefly has become a core part of our lab's knowledge base and a great help to me in training researchers and lab management". Dr Gena Nikitin, founder of biotech business Miphic, adds: "It is the future of foolproof knowledge-sharing between scientists." Investors in the business are also excited. Today's pre-seed round, aimed at providing Briefly Bio with the funding to roll out its software commercially, is led by Compound VC, with participation from NP Hard, Tiny VC and a number of angel investors. "The crux of successful science lies in consistent and executable methods," says Shelby Newsad, an investor at Compound VC. "Whereas most bio software companies focus on data and its analysis, Briefly goes upstream to the core problem space of reproducibility; this incentivises scientists to share more of their previously tacit knowledge."

Forbes

Wed, 17 Jul, 8:01 AM UTC

AI in Pharma and Biotech Market Projected Growth at 18.8% CAGR to Reach USD 13.1 billion by 2034 | Exclusive Research Report by Transparency Market Research Inc.

Wilmington, Delaware, United States, Transparency Market Research Inc. -, Aug. 29, 2024 (GLOBE NEWSWIRE) -- A global AI in pharma and biotech industry (제약 및 생명공학 산업의 AI) valuation of US$ 1.8 billion was estimated for 2023. The market will reach US$ 13.1 billion by 2034, with a CAGR of 18.8% between 2024 and 2034. Healthcare could be transformed entirely by artificial intelligence in the field of medication creation. New medicines for illnesses are expensive, time-consuming, and often fraught with failure. It can take up to ten years to develop a new medicine. Drugstore shelves rarely contain the majority of promising medications developed in laboratories. The use of deep learning algorithms, generative models, and advanced data analytics will further revolutionize the process of drug discovery by identifying novel drug candidates, forecasting their safety and efficacy profiles, and improving their properties. With the application of artificial intelligence (AI) and techniques such as virtual screening, high-throughput screening, and molecular modeling, the identification and optimization of lead compounds can be accelerated while saving money and time compared with traditional approaches. For More Details, Request for a Sample of this Research Report: https://www.transparencymarketresearch.com/ai-in-pharma-and-biotech-market.html Artificial intelligence (AI) will continue to advance precision medicine, allowing individualized treatment plans to be developed based on the unique genetic makeup, molecular fingerprints, and clinical characteristics of each patient. Using multi-omics data, electronic health records, and empirical evidence, machine learning algorithms can forecast the course of a disease and determine the best treatment course for a patient. Key Findings of the Market Report The pharmaceutical & biotech segment held the largest share in 2023AI-driven biotechnology was most prevalent in North America in 2023.Increasing adoption of AI-based drug discovery tools in Canada to promote precision medicine.Based on applications, drug discovery and development will dominate the market over the next few years. Global AI in Pharma and Biotech Market: Growth Drivers Using massive amounts of biological data to predict the safety and efficacy profiles of promising drug candidates, artificial intelligence (AI) accelerates drug discovery. As an alternative to conventional methods, it saves time and money by expediting the discovery of drug targets, optimizing leads, and predicting toxicity.With AI, tailored treatment plans can be developed by evaluating patient data, such as genetic information, medical imaging data, and electronic health records, to determine the best drugs for each patient's condition. Through this method, medical resources are allocated efficiently, side effects are minimized, and treatment results are enhanced.Analyzing patient data with artificial intelligence (AI) helps predict adverse events, treatment outcomes, and disease progression. Clinical trials are designed, patients are recruited, and monitored better, which speeds them up, lowers costs and increases success rates.The use of artificial intelligence (AI) algorithms can uncover new therapeutic uses for drugs based on their molecular structure, biological target and clinical data. Through this method, new treatments for new indications can be developed more quickly and with lower risks and costs. Global AI in Pharma and Biotech Market: Regional Landscape North America is expected to lead the market for AI in pharma and biotech market (الذكاء الاصطناعي في سوق الأدوية والتكنولوجيا الحيوية). Leading research centers, academic institutions, and tech companies in North America are driving artificial intelligence (AI), machine learning, and data analytics.Advanced AI platforms and solutions that are appropriate for clinical research, drug discovery, and healthcare delivery can be developed based on this technological expertise.Biotech and pharmaceutical industries are thriving in North America with many well-established businesses and start-ups involved in drug discovery, development, and commercialization. As these industry leaders promote AI technology adoption, productivity is improved, innovation is stimulated, and international competition is maintained.Health Canada, the National Institutes of Health (NIH), and the National Science Foundation (NSF) in the United States provide substantial funding and support for AI-driven research programs in healthcare and life sciences.Regulatory measures, grant programs, and research collaborations promote the integration of AI into biotech and pharmaceutical operations while guaranteeing adherence to safety and ethical norms. Global AI in Pharma and Biotech Market: Competitive Landscape As manufacturers seek to strengthen their position in the AI market, they are pursuing both organic and inorganic strategies. Clinical trial simulation platforms are being explored by the key players in AI in the pharmaceutical and biotechnology market as a means of fast-tracking clinical trials and improving patient care. Key Players Profiled BenevolentAI SAInsilico MedicineRecursion PharmaceuticalsAtomwise Inc.Berg LLCCloud PharmaceuticalsDeep GenomicsExscientiaSchrödingertwoXar Inc.Insitro Key Developments In March 2024, Nvidia introduced 21 generative AI microservices designed to promote healthcare innovation. It is expected that the 25 genAI microservices will enhance healthcare organizations' ability to discover drugs, conduct medical imaging, analyze genomic data, communicate with people, and develop digital human initiatives.In July 2023, Recursion partnered with Nvidia for $50 million in AI-powered drug discovery. Nvidia has funded and teamed up with Recursion to develop "AI-training-as-a-service" software for drug discovery on the Nvidia DGX Cloud. For Complete Report Details, Request Sample Copy from Here- https://www.transparencymarketresearch.com/ai-in-pharma-and-biotech-market.html Global AI in Pharma and Biotech Market: Segmentation Application Drug Discovery and DevelopmentClinical Trials and OptimizationPredictive Maintenance and Quality ControlDrug Target IdentificationDisease Diagnosis & PrognosisOthers End User Pharmaceutical & Biotech CompaniesAcademic & Contract Research OrganizationsOthers Region North AmericaEuropeAsia PacificLatin AmericaMiddle East & Africa Explore TMR's related ongoing Coverage on Healthcare Market Insights Domain: Drug Discovery Informatics Market (創薬情報市場) - The global drug discovery informatics market is estimated to flourish at a CAGR of 10.9% from 2023 to 2031. Transparency Market Research projects that the overall sales revenue for drug discovery informatics is estimated to reach US$ 6.8 billion by the end of 2031. Medical Aesthetic Devices Market (Markt für medizinische ästhetische Geräte)- The global medical aesthetic devices market is estimated to flourish at a CAGR of 7.1% from 2023 to 2031. Transparency Market Research projects that the overall sales revenue for medical aesthetic devices is estimated to reach US$ 25.1 billion by the end of 2031. About Transparency Market Research Transparency Market Research, a global market research company registered at Wilmington, Delaware, United States, provides custom research and consulting services. Our exclusive blend of quantitative forecasting and trends analysis provides forward-looking insights for thousands of decision makers. Our experienced team of Analysts, Researchers, and Consultants use proprietary data sources and various tools & techniques to gather and analyses information. Our data repository is continuously updated and revised by a team of research experts, so that it always reflects the latest trends and information. With a broad research and analysis capability, Transparency Market Research employs rigorous primary and secondary research techniques in developing distinctive data sets and research material for business reports. Transparency Market Research Inc. CORPORATE HEADQUARTER DOWNTOWN, 1000 N. West Street, Suite 1200, Wilmington, Delaware 19801 USA Tel: +1-518-618-1030 USA - Canada Toll Free: 866-552-3453 Website: https://www.transparencymarketresearch.com Email: sales@transparencymarketresearch.com Follow Us: LinkedIn| Twitter| Blog | YouTube Market News and Data brought to you by Benzinga APIs

Benzinga

Thu, 29 Aug, 6:05 AM UTC

How A.I. Is Disrupting Pharma: The High-Stakes Race to Revolutionize Drug Discovery

As the FDA prepares to regulate A.I. in drug development, biotech startups and pharma giants are racing to harness its potential for faster, more precise drug discovery. For better or worse, A.I. in the pharmaceutical industry has reached a fever pitch -- a point of no return. Ever cautious, the Food and Drug Administration (FDA) has stepped in to regulate the burgeoning use of A.I. in drug discovery and development, vowing to "promote innovation and protect patient safety." But while the agency ponders policy, companies like Immunai have already charged ahead. The biotech company recently inked an $18 million deal with AstraZeneca, which aims to harness Immunai's A.I.-powered immune system model to optimize clinical trials. Startups like Insilico Medicine and Recursion Pharmaceuticals tout A.I. as their secret weapon in the hunt for new drugs, though skeptics whisper that their claims smack of A.I. washing. Sign Up For Our Daily Newsletter Sign Up Thank you for signing up! By clicking submit, you agree to our <a href="http://observermedia.com/terms">terms of service</a> and acknowledge we may use your information to send you emails, product samples, and promotions on this website and other properties. You can opt out anytime. See all of our newsletters "There is a spectrum of opinions on whether A.I. is going to solve problems on its own in drug development, versus whether it's not going to be relevant and feasible at all. I think I sit somewhere in the middle," Raviv Pryluk, co-founder and CEO of PhaseV, tells Observer. Pryluk, who previously served as SVP of operations and analytics at Immunai, struck out on his own in 2023 to launch a company focused on using A.I. and machine learning to streamline clinical trials. "It can bring a tremendous amount of value, but only when done carefully," he cautions. The FDA claims that since 1995, it has received over 300 submissions for drugs and biological products containing A.I. components. Yet the complexity -- and promise -- of A.I. has grown exponentially, says Dave Latshaw II, formerly the head of A.I. drug development at Johnson & Johnson. "It's a bit different these days, primarily through the scale and quality of data that we have access to," Latshaw tells Observer. In practice, A.I. has already reshaped clinical trials, from pairing patients with studies to targeting specific populations. But there's a catch: while narrowing down patient types might yield more accurate results, it also restricts the commercial appeal of a drug. "They want to find the exact type of person their drug will be useful for but, at the same time, doing that limits the scope of the total population it will be useful for and limits the total commercial impact," Pryluk says. Latshaw, who left Johnson & Johnson in 2020 to co-found BioPhy, sees an ongoing tension between technological advancement and commercial priorities. He launched BioPhy to address the inefficiencies he couldn't touch within J&J's bureaucratic walls. For him, the real promise of A.I. is in refining the entire drug discovery pipeline, including clinical trial optimization. Mergers and acquisitions also present a tantalizing opportunity. Pharmaceutical giants often rely on acquisitions to bolster their portfolios, and A.I. can sift through troves of pre-clinical and clinical data to spotlight the most promising drugs. "Machine learning and A.I. can help run over the pre-clinical and clinical data and all the available literature in order to make better decisions on whether to acquire this drug versus that drug," Pryluk notes. A.I. could also help diversify clinical studies, a longstanding challenge in the industry. Since 1993, the National Institutes of Health (NIH) has mandated the inclusion of women in research, but gender gaps -- particularly in preclinical animal studies -- remain a stubborn problem. Researchers could tailor studies using advanced methods like causal machine learning to ensure that findings apply across demographics. "What if we can use more sophisticated and granular methods like causal machine learning, for example, and ask causal questions on whether the results that we've seen on subsets of women patients actually can tell us something, and then we can expand the population?" Pryluk asks. "I think this type of method can bring more women, more people from minorities, to participate in clinical trials." Pryluk believes that precision medicine -- treating each patient as a unique case -- will be the industry's next seismic shift. Of course, there's still plenty of skepticism. Every A.I. application in pharma has to survive a gauntlet of risk-benefit analysis. Data sharing, cybersecurity, inscrutable algorithms and biases pose perennial challenges. For instance, genetic data -- the lifeblood of many A.I. models -- raises significant privacy concerns. Just look at the ongoing debates around platforms like 23andMe. Steven Aviv, CTO of Pentavere, sees data trust as paramount. Pentavere's DARWEN A.I. sifts through unstructured healthcare data to identify patients for specific treatments. "Pharmaceutical customers tell us that trust in the data for these applications is imperative," Aviv tells Observer. That's why Pentavere leans on Databricks' Data Intelligence Platform, he says. The secure framework is designed to handle massive data volumes without compromising compliance. Latshaw, too, emphasizes transparency in A.I. solutions. Interpretability is key, he says, noting that researchers risk misinterpreting A.I.'s outputs without it. "There's always a motivation for publishing the next even moderately state-of-the-art method for something, but sometimes you have to ask if the question you're addressing in the research is actually the right question," Latshaw cautions. "Just because you can predict the structure of the protein doesn't mean you can accurately predict what its function is going to be and how that impacts the disease." Pryluk is blunt about the stakes. "We are not in a game. We are talking about patients. People are dying and it needs to be practical," he says. Despite the inevitable setbacks, Latshaw is bullish on A.I.'s long-term impact. "There's going to be failures in the space as there always are when people push the envelope," he says. "But there's going to be an incredible amount of learning from that, and the byproduct is going to be higher productivity in discovering new molecules." As the FDA debates regulation, it's inviting input from industry stakeholders. Latshaw reminds us that the agency's goal is patient protection, not industry obstruction. "If they see the industry wanting to use new technology, it's their responsibility to make sure it's used in a way that doesn't impact that overall goal," he says. "The strength of that regulation will depend largely on how much of an impact the technology will have on the end recipient." In Latshaw's view, the payoff could be transformative: over the next 5-10 years, he expects a boom in viable drug candidates reaching clinical trials. "What that means for the companies is they're going to be able to bring a lot more drugs to market, but also get that many more drugs to the patients that need them," he says.

Observer

Tue, 12 Nov, 2:36 PM UTC

AI in Drug Discovery: Insitro CEO Discusses Potential and Challenges

Daphne Koller, CEO of Insitro, explains how AI and machine learning could revolutionize drug discovery, potentially accelerating the development of new medicines and overcoming longstanding industry challenges.

4 Sources

Tue, 3 Dec, 12:06 AM UTC

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