Overcoming Common Roadblocks in Biopharma to Harness the Power of AI

Overcoming Common Roadblocks in Biopharma to Harness the Power of AI: Insights from RCH Solutions

In the rapidly evolving field of life sciences, artificial intelligence (AI) has emerged as a transformative force, promising to revolutionize biopharmaceutical research and development. However, many biopharma companies, regardless of their size, encounter significant roadblocks that hinder the effective integration and utilization of AI. As a specialized scientific computing provider with an exclusive focus on the life sciences, RCH Solutions has identified several common challenges and offers strategies to overcome these obstacles, enabling organizations to fully leverage the power of AI.

Common Roadblocks in Biopharma

Data Silos and Fragmentation: One of the most pervasive issues in biopharma organizations is the existence of data silos, where valuable data is isolated across different departments or systems. This fragmentation makes it difficult to aggregate, analyze, and derive insights from data, which is essential for effective AI implementation.
Data Quality and Standardization: Poor data quality and lack of standardization are significant barriers to AI adoption. Inconsistent data formats, incomplete datasets, and erroneous information can lead to inaccurate AI models, reducing their reliability and effectiveness.
Integration with Existing Systems: Integrating AI solutions with existing IT infrastructure and legacy systems can be complex and costly. Many biopharma companies struggle with ensuring seamless integration, which is crucial for leveraging AI across various stages of research and development.
Skills and Expertise Gap: The successful implementation of AI requires specialized skills and expertise in both AI technologies and life sciences. Many biopharma companies face a shortage of talent with the necessary interdisciplinary knowledge to develop and deploy AI solutions effectively.
Regulatory and Compliance Challenges: The highly regulated nature of the biopharma industry poses additional challenges for AI adoption. Ensuring that AI solutions comply with stringent regulatory requirements and maintaining data privacy and security are critical concerns that must be addressed.

Strategies to Overcome These Roadblocks

Breaking Down Data Silos: To address data silos, biopharma companies should invest in data integration platforms that enable seamless data sharing across departments. RCH Solutions advocates for the implementation of centralized data repositories and the use of standardized data formats to facilitate data aggregation and analysis.
Enhancing Data Quality and Standardization: Implementing robust data governance frameworks is essential to ensure data quality and standardization. This includes establishing data validation processes, using automated data cleaning tools, and enforcing standardized data entry protocols. RCH Solutions emphasizes the importance of a strong data governance strategy to support reliable AI models.
Seamless Integration with Existing Systems: Biopharma companies should adopt flexible and scalable AI solutions that can integrate smoothly with their existing IT infrastructure. RCH Solutions recommends leveraging cloud-based platforms and APIs that facilitate integration and interoperability, reducing the complexity and cost of deploying AI technologies.
Bridging the Skills Gap: Addressing the skills gap requires a multifaceted approach, including investing in training and development programs, partnering with academic institutions, and hiring interdisciplinary experts. RCH Solutions also suggests collaborating with specialized AI vendors and consulting firms to access the required expertise and accelerate AI adoption.
Navigating Regulatory and Compliance Requirements: Ensuring regulatory compliance involves staying abreast of evolving regulations and implementing robust data security measures. RCH Solutions advises biopharma companies to work closely with regulatory experts and incorporate compliance checks into their AI development processes. Adopting secure data management practices and ensuring transparency in AI models are also critical for meeting regulatory standards.

Use Cases of AI in Biopharma

Drug Discovery and Development: AI can significantly accelerate drug discovery by identifying potential drug candidates, predicting their efficacy, and optimizing drug design. For example, AI algorithms can analyze large datasets of chemical compounds and biological targets to identify promising drug candidates, reducing the time and cost associated with traditional drug discovery methods.
Clinical Trial Optimization: AI can enhance the efficiency of clinical trials by predicting patient responses, identifying suitable participants, and optimizing trial designs. Machine learning models can analyze patient data to predict outcomes and stratify patients, improving the success rates of clinical trials.
Personalized Medicine: AI enables the development of personalized treatment plans by analyzing patient data, including genomic information, to identify the most effective therapies for individual patients. This approach can lead to better patient outcomes and more efficient use of healthcare resources.
Operational Efficiency: AI can streamline various operational processes within biopharma companies, such as supply chain management, manufacturing, and quality control. Predictive analytics and AI-driven automation can optimize these processes, reducing costs and improving overall efficiency.

Conclusion

The integration of AI in biopharma holds immense potential to transform research, development, and operational processes. However, overcoming common roadblocks such as data silos, poor data quality, integration challenges, skills gaps, and regulatory hurdles is crucial for realizing this potential. By implementing strategic solutions and leveraging the expertise of specialized scientific computing providers like RCH Solutions, biopharma companies can successfully harness the power of AI to drive innovation and achieve their scientific and business objectives.

For more insights and support on integrating AI in your biopharma organization, visit RCH Solutions.

AI-Aided Drug Discovery and the Future of Biopharma

Overcoming Operational Challenges with AI Drug Discovery

While computer-assisted drug discovery has been around for 50 years, the need for advanced computing tools has never been so crucial.

AI-Aided Drug Discovery Today, machine learning is proving invaluable in managing some of the intricacies of R&D and powering breakthroughs thanks to its ability to process millions of data points in mere seconds.

Of course, AI drug discovery and development tools have their own complex operational demands. Ensuring their integration, operation, and security requires high-performance computing and tools that help manage and make sense of massive data output.

Aligning Biochemistry, AI, and system efficiency

The process of creating and refining pharmaceuticals and biologics is becoming more complex, precise, and personalized, largely due to the robust toolkit of artificial intelligence. As a result, combining complex scientific disciplines, AI-aided tools, and expansive IT infrastructure has come to pose some interesting challenges.

Now, drug discovery and development teams require tools and AI that can:

Optimize data and applicable storage and efficiently preprocess massive molecular datasets.
Support high-throughput screening as it sifts through millions of molecular predictions.
Enable rapid and accurate prediction of molecular attributes.
Integrate large and diverse datasets from clinical trials, genomic insights, and chemical databases.
Scale up as computational power as demands surge.

Challenges in Bio-IT for drug discovery and development

Drug discovery and development calls for a sophisticated toolset. The following challenges demonstrate the obstacles such tools must overcome.

The magnitude and intricacy of the molecular datasets needed to tackle the challenges of drug discovery and development require more than storage solutions. These solutions must be tailored to the unique character of molecular structures.
High-throughput screening (HTS)—a method that can rapidly test thousands to millions of compounds and identify those that may have a desired therapeutic effect—also requires immense processing power. Systems must be capable of handling immediate data feeds and performing fast, precise analytics.
Predicting attributes for millions of molecules isn’t just about speed; it’s about accuracy and efficiency. As a result, the IT infrastructure must be equipped to handle these instantaneous computational needs, ensuring there are no delays in data processing, which could bottleneck the research process.
The scalability issue extends far beyond capacity. Tackling this requires foresight and adaptability. Planning for future complexities in algorithms and computation means pharma teams need a robust and adaptive infrastructure.
Integrating data into a holistic model poses significant challenges. Teams must find ways to synthesize clinical findings, genomic insights, and chemical information into a unified, coherent data model. This requires finding tech partners with expertise in AI-driven systems and data management strategies; these partners should also recognize and address the peculiarities of each domain, all while providing options for context-driven queries.

As we can see, high-level Bio-IT isn’t just an advantage; it’s a necessity. And it’s one that requires the right infrastructure and expertise from an experienced IT partner.

Mastering the Machine Learning Workflow

Bridging the nuances of drug discovery with the technicalities of artificial intelligence demands specialized knowledge, including:

Machine learning algorithms. Each drug discovery dataset has unique characteristics, and the AI model should mirror these idiosyncrasies. Initial testing in a sandbox environment ensures scalability and efficiency before amplification across larger datasets.
Data preprocessing. High-quality data drives accurate predictions. Effective preprocessing ensures datasets are robust, balanced, capable of interpolating gaps, and free from redundancies. In the pharmaceutical realm, this is the bedrock of insightful machine-learning models.
Distributed computing. When handling petabytes of data, traditional computational methods may falter. Enter distributed computing. Platforms like Apache Spark enable the distributed processing essential for the seamless analysis of massive datasets and drawing insights in record time.
Hyperparameter tuning. For pharma machine learning models, tweaking hyperparameters is key to the best performance. The balancing act between trial-and-error, Bayesian optimization, and structured approaches like grid search can dramatically impact model efficiency.
Feedback mechanisms. Machine learning thrives on feedback. The tighter the loop between model predictions and real-world validations, the sharper and more accurate the predictions become.
Model validation. Ensuring a model’s robustness is critical. Cross-validation tools and techniques ensure that the model generalizes well without losing its specificity.
Integration with existing Bio-IT systems. Interoperability is key. Whether through custom APIs, middleware solutions, or custom integrations, models must be seamlessly woven into the existing IT fabric.
Continuous model training. The drug discovery landscape is ever-evolving. Models require a mechanism that constantly feeds new insights and allows them to evolve, adapt, and learn with every new dataset.

Without the right Bio-IT infrastructure and expertise, AI drug discovery cannot reach its full potential. Integrating algorithms, data processing, and computational methods is essential, but it’s their combined synergy that truly sparks groundbreaking discoveries.

Navigating Bio-IT in drug discovery

As the pharmaceutical industry advances, machine learning is guiding drug discovery and development to unprecedented heights through enabling the creation of sophisticated data models.

By entrusting scientific computing strategies and execution to experts who understand the interplay between research, technology, and compliance, research teams can remain focused on their primary mission: groundbreaking discoveries.

Get in touch with our team if you’re ready to start a conversation about harnessing the full potential of Bio-IT for your drug discovery endeavors.

Living Up to the Promises of AI-Aided Drug Discovery

Implementation and interoperability are key to achieving the benefits of AI in pharma.

Artificial intelligence is showing great promise in streamlining the development of new pharmaceuticals. In fact, a recent LinkedIn poll revealed that AI and emerging tech was the leading opportunity area identified for pharma R&D. But it’s not a silver bullet—implementing AI technologies comes with a range of complexities, especially in aligning them with the existing challenges of drug development. For AI-aided drug discovery to work, pharma companies need the right solutions, support, and expertise to gain the most benefit.

The Promising Future of AI-Aided Drug Discovery

Drug discovery is an incredibly complex, laborious, costly, and lengthy process. Traditionally, it requires extensive manual testing and documentation. On average, a new treatment costs $985 million to develop, with a high trial failure rate being a leading cause of sky-high costs. In fact, only about one in eight treatments that enter the clinical trial phase make it to the market, while the remaining seven are never developed.

AI has the ability to analyze significant volumes of data, predict outcomes, and uncover data similarities to help drive down costs. Making connections between data points in real-time boosts efficiency and reduces the time to discovery—that is, when AI technologies are implemented properly.

AI Challenges That Impact Drug Discovery

The advancement of AI and machine learning is showing great potential in combating some, if not all, of the challenges of traditional drug discovery. But AI-aided drug discovery also invites new challenges of its own.

One such challenge is the potential lack of data to properly feed AI and machine learning technologies. Typically, AI relies on large datasets from which to “learn.” But with unique diseases and rare conditions, there simply isn’t a lot of data for these technologies to ingest. What’s more, these tools typically need years of historical data to identify trends and patterns. Given the frequency of mergers and acquisitions in pharma, original data sources may be unavailable and, therefore, unusable.

McKinsey notes that one of the greatest challenges lies in delivering value at scale. AI should be fully integrated into the company’s scientific processes to gain the full benefit of AI-driven insights. AI-enabled discovery approaches (including via partnerships) are often kept at arm’s length from internal day-to-day R&D. They proceed as an experiment and are not anchored in a biopharma companies’ scientific and operational processes to achieve impact at scale.

Additionally, achieving interoperability limits the effectiveness of AI in drug research. Investment in digitized drug discovery capabilities and data sets within internal R&D teams is minimal. Companies frequently leverage partner platforms and enrich their IP rather than build biopharma’s end-to-end tech stack and capabilities. However, data needs to break out of silos and communicate with each other to contextualize the outputs. This is easier said than done when data comes from multiple sources in different structures and varying levels of reliability.

A part of this bigger challenge is the lack of data standardization. Using AI in drug discovery is still very new. The industry as a whole has not defined what constitutes a good data set, nor is there an agreed-upon set of data points that should be included in R&D processes. This opens the door for data bias, especially as some groups of the population have historically been omitted from medical datasets, which could lead to misdiagnoses or unreliable outcomes.

A lack of standardization also invites the potential for regulatory hurdles. Without a standardized way to structure, capture, and store data, pharma companies could be at risk of privacy concerns or non-compliance. The pharma industry is heavily regulated and requires careful documentation and disclosures at every stage of drug development. Adding the AI element to the process will introduce new regulatory considerations to ensure safety, privacy, and thoroughness.

How to Gain Support for AI-Aided Drug Discovery

AI is the future of daily human living—from how we travel, to what we buy, to the pharmaceuticals we take to live a higher quality of life. But in Life Sciences, AI will not replace Research Scientists, but Research Scientists that use AI will replace those that don’t. And Biotechs and Pharma companies conducting drug discovery and development need an experienced partner that understands that, for the effective implementation of AI technologies that drive results.

If your organization is looking to incorporate AI to boost your drug discovery goals, a strategic partner will help you navigate and circumvent the unavoidable hurdles and pitfalls from inception. At RCH Solutions, our Bio-IT consultants in Life Sciences understand the intricacies of the pharma industry and how they relate to the use of new technology. Implement new solutions in an intentional manner and give them staying power to achieve the greatest possible outcomes.

Download the Emerging Technologies eBook to learn more about the future of AI-aided drug discovery, and get in touch with our team for a consultation.

Sources:

https://www.mckinsey.com/industries/life-sciences/our-insights/ai-in-biopharma-research-a-time-to-focus-and-scale

https://www.spiceworks.com/tech/artificial-intelligence/guest-article/top-challenges-faced-by-pharma-ai/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054832/

https://www.clinicalleader.com/doc/data-interoperability-the-first-step-to-leverage-ml-ai-in-clinical-trials-0001

https://roboticsbiz.com/ai-in-drug-discovery-benefits-drawback-and-challenges/

https://arxiv.org/abs/2212.08104

https://www.weforum.org/agenda/2022/10/open-source-data-science-bias-more-ethical-ai-technology/

How Can Life Science Researchers Balance Security with Emerging Technology in Life Science Research?

Data is the currency of scientific research. Its security should not be left to chance.

Data integrity is crucial across all forms of research, including Life Sciences research. After all, it’s the only way researchers and regulators can assure the quality, safety, and efficacy of their products.

The way Life Science companies store and communicate data has become increasingly crucial to validating the expectation that their data is safe and secure; as a result, organizations must be hyper-vigilant in mitigating data risks like cyberattacks, data breaches, and record falsification.

In fact, these expectations have grown in recent years. As the Life Science industry grows in complexity, the use of highly automated Cloud-enabled systems makes data integrity increasingly important to sustainable success. Compliance needs are driving organizations to make their data-related processes more robust and secure.

It takes more than controls, processes, and technology to implement good data practice. Life Science research firms must adopt a wider shift towards educating for data risk mitigation and develop a culture that understands and values data integrity.

5 Key Elements of Data Integrity

Life Science research data needs to be complete, consistent, and accurate throughout the data lifecycle. Ensuring that all original records and true copies – including source data and metadata – remain un-compromised in the Life Science environment is no small feat. It is important to focus on five key characteristics to increase data integrity:

These characteristics ensure that data is complete, consistent, enduring, and available. Once Life Science research firms implement solutions that maintain data integrity, they can begin operating in more risk-intelligent ways.

Life Science Data Risks are Unique

Several factors combine to give Life Science research a unique risk profile. While many of the threats that Life Science organizations face are the same ones faced by the commercial and government sectors, there are structural risks inherent to the way Life Science research must be carried out.

Intellectual properties in the Life Sciences are incredibly valuable. Drug formulas, medical device blueprints, and clinical data are the result of years of painstaking research. These properties may have life-changing patient impacts and the potential to generate billions of dollars in revenue. Understandably, these assets are of enormous interest to hackers, including attackers sponsored by hostile nation-states.

As relevant as is the issue of hackers, the internal risk is something to combat, as well. Research teams often exchange sensitive information within different work streams, and among a wide range of partners. While sharing data expedites research and development, it also increases the risk of data falling into the wrong hands. Even within the field, it is important to be aware of potentially untrustworthy sources with or without malicious intent.

Life Science organizations typically rely on a global network of suppliers for hard-to-find materials and equipment. Supply chain attacks – where attackers exploit a weak link in a trusted vendor to infiltrate organizations down the supply chain – are a dangerous and growing trend.

Mergers and acquisitions within the Life Science industry also have a tendency to increase security risks. When two companies merge, they inevitably share data in a trust-oriented environment. If both companies’ IT teams have not taken sufficient action to secure that environment first (or adopted a zero-trust model), new vulnerabilities may come to light.

Implement Cloud Security and Risk Mitigation Strategies

Life Science researchers do not have to give up on the significant advantages that Cloud technology offers. They simply must plan for security contingencies that reflect today’s data risk environment.

Mitigating Cloud risk means establishing a robust cybersecurity policy that doesn’t simply conform to industry standards, but exceeds them. Beyond well-accepted methods like multi-factor authentication, full data encryption (in-transit and at rest) and data exfiltration add layers of protection but require adopting a more proactive stance towards security as a tenet of workplace culture. For example, it’s critical that teams manage encryption keys, fine-grain security, and network access controls internally (vs. outsourcing to the Public Cloud provider). Additionally, work-flow controls and empowered data stewards help put controls in place with reduced impact(s) to collaborative work.

In summary, every research position is also a cybersecurity position. Teaching team members to maintain data integrity ensures secure, consistent access to innovative technologies like the Cloud.

Get on Board with Emerging Biopharma Technology

Explore the cutting-edge tools revolutionizing the industry.

Computer-aided design (CAD) is already an essential tool for most biopharmaceutical research teams. But as CAD tools such as deep learning (DL), machine learning (ML), and artificial intelligence (AI) evolve faster than we can imagine, even the most tech-savvy teams risk missing out on the transformational benefits of these state-of-the-art tools. Explore the nearly unimaginable advantages of the latest CAD advancements and learn how to break down barriers to unlocking the full potential of these innovations, even if your organization has yet to reach for the key.

The Capabilities of Cutting-Edge CAD

Experts in AI, ML, and DL have documented a few primary areas of digital transformation within the biopharmaceutical realm.

Drug discovery

ML gives labs the ability to quickly process nearly infinite drug datasets to find matches that could fuel the discovery of new pharmaceutical treatments.

As reported by Genetic Engineering & Biotechnology News, the firm Atomwise pioneered the use of convolutional neural networking—a form of ML used in common consumer tech like social media photo tagging—for more than 550 initiatives, including drug discovery, dose optimization, and toxicity screening.

Personalized medicine

This term describes the use of a type of ML called predictive analytics, in which a patient’s individual health data provides the physician with detailed information about their genetics, risks, and possible diagnoses. The partnership between IBM Watson Oncology and Memorial Sloan Kettering exemplifies the area of personalized medicine as they drive research into the use of this modality to optimize patient treatments and outcomes. The availability of mobile apps, devices, and biosensors for remote health analysis will dramatically expand this area in the coming years.

Patient and site selection for clinical trials

Biopharma companies can significantly reduce the cost and time investment of clinical trials with the application of ML algorithms. In a 2018 article, research firm Evidera cited an analysis of 151 international clinical trials at nearly 16,000 sites. The study, which appeared in the journal Therapeutic Innovation and Regulatory Science, uncovered the difficulty of finding appropriate patients for clinical trials, especially for central nervous system conditions. The use of AI models can potentially use data mining to find subjects that have not yet been diagnosed with the disease in question.

Overcoming barriers to focus on the future

Research institutions of all sizes struggle to adopt emerging tech. The most common blockades to biopharma progress in this area include:

Internal culture that resists innovation
Limited infrastructure and resources to invest in technology
Misplaced commitment to legacy tools and practices that prevent experimentation
Lack of digital leadership from C-level executives
Limited access to clean, reliable datasets
Barriers to interoperability among collaborators, often even within the same organization
Concerns about data privacy and security, as well as other regulatory issues

The ethical implications of artificial intelligence and machine learning also pose issues, as technology evolves faster than we can answer questions about bias, transparency, and related concerns.

Today’s emerging biopharma tech will rapidly evolve and replace itself with tomorrow’s innovations. Within just a few years, modern labs that realize the possibilities of AI, DL, and ML will leave traditional biopharma firms in their dust with little hope of recovery. For example, companies that do not adopt advanced AI methods to recruit participants for clinical trials will struggle to complete the necessary research to produce new products. Deloitte estimates that fewer than 17 percent of the 30,000 trials registered on ClinicalTrials.gov in 2018 ever published results.

In a 2019 study by Deloitte Insights, 79 percent of biotech respondents said their companies planned to implement new CAD advancement within the next five years, with 58 percent citing digital transformation as a top leadership priority. The prior year, a benchmarking study by the research firm found that 60 percent of biotech firms used machine learning, and 96 percent anticipated using it in coming years.

AI Ecosystems, Edge, and the Potential for Quantum Computing in Research Science

Key Takeaways from NVIDIA’s GTC Conference Keynote

I recently attended NVIDIA’s GTC conference. Billed as the “number one AI conference for innovators, technologists, and creatives,” the keynote by NVIDIA’s always dynamic CEO, Jensen Huang, did not disappoint.

Over the course of his lively talk, Huang detailed how NVIDIA’s DGX line, which RCH has been selling and supporting since shortly after the inception of DGX, continues to mature as a full-blown AI enabler.

How? Scale, essentially.

More specifically, though, NVIDIA’s increasing lineup of available software and models will facilitate innovation by removing much of the software infrastructure work and providing frameworks and baselines on which to build.

In other words, one will not be stuck reinventing the wheel when implementing AI (a powerful and somewhat ironic analogy when you consider the impact of both technologies—the wheel and artificial intelligence—on human civilization).

The result, just as RCH promotes in Scientific Compute, is that the workstation, server, and cluster look the same to the users so that scaling is essentially seamless.

While cynics could see what they’re doing as a form of vendor lock, I’m looking at it as prosperity via an ecosystem. Similar to the way I, and millions of other people around the world, are vendor-locked into Apple because we enjoy the “Apple ecosystem”, NVIDIA’s vision will enable the company to transcend its role as simply an emerging technology provider (which to be clear, is no small feat in and of itself) to become a facilitator of a complete AI ecosystem. In such a situation, like Apple, the components are connected or work together seamlessly to create a next-level friction-free experience for the user.

From my perspective, the potential benefit of that outcome—particularly within drug research/early development where the barriers to optimizing AI are high—is enormous.

The Value of an AI Ecosystem in Drug Discovery

The Cliff’s Notes version of how NVIDIA plans to operationalize its vision (and my take on it), is this:

Application Sharing: NVIDIA touted Omniverse as a collaborative platform — “universal” sharing of applications and 3D.
Data Centralization: The software-defined data center (BlueField-2 & 3 / DPU) was also quite compelling, though in the world of R&D we live in at RCH, it’s really more about Science and Analytics than Infrastructure. Nonetheless, I think we have to acknowledge the potential here.
Virtualization: GPU virtualization was also impressive (though like BlueField, this is not new but evolved). In my mind, I wrestle with virtualization for density when it comes to Scientific Compute, but we (collectively) need to put more thought into this.
Processing: NVIDIA is pushing its own CPU as the final component in the mix, which is an ARM-based processor. ARM is clearly going to be a force moving forward, and Intel x86_64 is aging … but we also have to acknowledge that this will be an evolution and not a flash-cut.

What’s interesting is how this approach could play to enhance in-silico Science.

Our world is Cloud-first. Candidly, I’m a proponent of that for what I see as legitimate reasons (you can read more about that here). But like any business, Public Cloud vendors need to cater to a wide audience to better the chances of commercial success. While this philosophy leads to many beneficial services, it can also be a blocker for specialized/niche needs, like those in drug R&D.

To this end, Edge Computing (for those still catching up, a high-bandwidth and very low latency specialty compute strategy in which co-location centers are topologically close to the Cloud), is a solution.

More interestingly, though, is how this strategy could help Life Sciences companies dip their toes into the still unexplored waters of Quantum Computing through cuQuantum … Quantum (qubit) simulation on GPU … for early research and discovery.

I can’t yet say how well this works in application, but the idea that we could use a simulator to test Quantum Compute code, as well as train people in this discipline, has the potential to be downright disruptive. Talking to those in the Quantum Compute industry, there are anywhere from 10 – 35 people in the world who can code in this manner (today). I see this simulator as a more cost-effective way to explore technology, and even potentially grow into a development platform for more user-friendly OS-type services for Quantum.

A Solution for Reducing the Pain of Data Movement

In summary, what NVIDIA is proposing may simplify the path to a more synergistic computing paradigm by enabling teams to remain—or become—Cloud-first without sacrificing speed or performance.

Further, while the Public Cloud is fantastic, nothing is perfect. The Edge, enabled by innovations like what NVIDIA is introducing, could become a model that aims to offer the upside of On-prem for the niche while reducing the sometimes-maligned task of data movement.

While only time will tell for sure how well NVIDIA’s tools will solve Scientific Computing challenges such as these, I have a feeling that Jensen and his team—like our most ancient of ancestors who first carved stone into a circle—just may be on to something here.

Choosing the Right Data Visualization Tools

Research labs equipped with the right tools and dashboards will have greater success in meeting drug discovery goals.

Research scientists in the pharmaceutical and biotech industries are increasingly turning towards state-of-the-art data visualization tools to leverage insights into their fields.

Up until recently, the vast majority of research scientists and Bio-IT professionals thought of data visualization purely as a presentation tool. Researchers tasked with justifying scientific funding to non-technical stakeholders would rely on a wide array of charts, graphs, and interactive software to communicate their arguments.

However, the immense growth in the volume of data generated by research processes has changed the landscape of data analytics in the laboratory. Scientists are using state-of-the-art visualization tools to gain deeper insights into the interplay of complex parameters under study.

Bio-IT research firms that invest in the right data visualization tools enjoy a lasting competitive edge. The ability to quickly glean insight from large, complex datasets is a key value-driver for research firms dedicated to new drug development.

How Data Visualization Helps Bio-IT Teams Gain Insight

Imagine a flock of migrating birds flying in formation across the autumn sky. In an instance, it’s obvious which direction they are headed. The emergent patterns of nature offer insight that would be much harder to obtain by analyzing every bird as an individual data point in a complex formula.

There is a clear need for drug discovery researchers to obtain new tools that make visualizing and analyzing data easier. As biomedical data become more diverse over time, this need will only grow in importance.

This is easy to see in drug discovery processes that involve the characterization of drug targets, small molecule leads, and their interactions. Immersive visualization and AI-powered deep learning algorithms can fundamentally change the way researchers approach molecular modeling and simulation. These are key steps to realizing the future of drug discovery,

The Limitations of Existing Software Tools

At RCH Solutions, we pride ourselves on leveraging the best talent and providing best-in-class cloud support for our customers. When we have AWS problems to solve, they go to our resident experts, Mohammad Taaha and Yogesh Phulke, both who have obtained AWS Solutions Architect certification.

Browser-based tools like ProViz and JalView display the sequence alignments of proteins, drawing from massive external databases. JalView can generate a 3D protein folding structure using a purpose-built molecular modeling engine called JMOL. These are widely used tools within the biotech and pharmaceutical industry, but they have severe limitations.

Inaccessibility is one of the main complaints that scientists level at the developers behind these tools. While undoubtedly powerful, they don’t offer a user-friendly interface and typically demand deep technical IT knowledge to use correctly. Every moment a researcher spends solving an IT-related obstacle is a moment not spent fulfilling his or her core function – making new discoveries.

In many cases, researchers find themselves taking data from one software tool and manually inputting it into another software tool. This is a time-consuming, error-prone task that delivers no additional value to the research process.

Bio-IT experts can automate these manual data entry tasks by integrating disparate systems using APIs. The better integrated your laboratory’s data infrastructure is, the faster and more efficiently its scientists will be able to work towards new discoveries.

Every research organization has a unique inventory of software tools, physical equipment, and data-oriented workflows. There is a clear need to design and develop an efficient system to make all of those elements work together smoothly.

Deploy Data Visualization Solutions for Your Biotech/Pharma Research Team

The ideal data structure and framework for each research organization is unique. Finding the right tools, dashboards, and platforms is only the first step towards achieving state-of-the-art data processing capabilities. Research executives who partner with Bio-IT experts can take the lead in designing, developing, and implementing systems that fit their workflows perfectly.

Research scientists need efficient, well-organized data visualization tools more urgently than ever.

Speak with a data expert about your software and hardware setup, and find out how you can cut waste and improve research outcomes for your entire team.

High-Performance Computing: The Power of Supercomputing in Drug Discovery

High-performance computing is a powerful weapon in the fight to find treatments and cures for all kinds of diseases.

Big hungry application beasts like Schrodinger, Amber, and Fastrocs, depending on how they are leveraged, provide keen insight on molecular level medicine allowing scientists to craft their own compounds.

Depending on whether you get your data from scopes, tests, databases or another source, it could take days or weeks for a single system to render a job to give you an answer, not to mention the risk of it failing and/or being rebooted, causing massive delays and awkward meetings.

That’s where High-Performance Computing (HPC) comes in. HPC allows you to run your jobs faster, more safely, and across an array of systems, processing the same large-scale and intricate jobs that typically take days for a single system, in mere hours.

And it’s scalable for need and usage.

But the power of HPC, like with any tool, comes with caveats.

Because of the complexity of the work it performs, HPC leverages hundreds of different add-on apps and modules to accomplish very specific tasks in a scientific research environment.

Many of these apps are created by scientific software companies and come with the full support of the company, and its development teams. Others are spun-up by firms or small groups of scientists to fit within their compute environment or perform tasks essential to their process. Still, others are created “one-off” by a single scientist or team to support the specific needs of a project on which they’re working at the time.

And therein lies the challenge with HPC: Though many of the apps that fall into those last two categories can and do prove to be useful to many scientists, optimizing and running them can be tricky. Without formal documentation, regular updates, and support, users often find themselves facing issues never before experienced by the small group who created the software, and with little recourse.

What’s a scientist to do? Find the person who built it? Figure out how to fix it on their own? Find something comparable that hopefully can accomplish the same goal?

All of these options are both speculative and time-consuming.

That’s because computing applications, particularly homegrown open-source apps, can be tricky at best.

For scientists to properly leverage these tools to move their research forward, not only do the solutions need to be implemented correctly, they need to be held up by a team with deep scientific and technical expertise, ready and able to test, evaluate, and build support knowledge.

And that is only one of the common challenges. The others are less obvious.

Think of it this way.

High-Performance Computing, as the name implies, is like a high-power dragster. Its engine is built perfectly to shatter speed records. Its body, carefully designed to cut effortlessly through the air. And its driver trained and skilled to steer it across the finish line gracefully.

The whole unit is built for high performance.

In a lab, the same principles must apply.

Workflow bottlenecks, throughput issues, and job optimization challenges must be eliminated. Additionally, you have to have the right environment upon which to build your HPC system in order for it to function at its best.

You can choose the cloud, which has many benefits. The cloud is great for scaling, testing, setting up solutions, burst computing, and multiple rapid setups of arrays, all without system maintenance.

You can choose a local on-prem build, which gives you more control over horsepower, predictability of costs, and a flexible security profile. (There are pros and cons to both options, so hybrid solutions are often the best choice — but what and how?)

Either way, and as you can see, there are a number of considerations critical for your ability to leverage the power of supercomputing in drug discovery.

The Value of an Experienced Partner

Implementing HPC is a massive project, particularly for research IT teams likely already overstretched.

Hiring more people can be time-consuming and costly. And pulling in a vendor can be risky. That is, unless it’s an established crew, with extensive experience and knowledge, and a deep bench full of talent. That saves teams time and money.

For almost 30 years, RCH Solutions has served that role. We’ve helped life sciences companies of all sizes clear the path to discovery by delivering scientific computing expertise and workflow best practices to support R&D teams.

If you’re looking for support in your HPC environment, learn how RCH can help your team.

Drug Treatment R&D Propelled Forward by AI and NVIDIA

AI training is driving HPC advances in drug discovery research.

Discovering innovative, life-saving drug treatments requires forging a path through unknown territory, with challenges around every corner. Very few treatments ever achieve fame; nine out of ten new therapies fail somewhere between phase I trials and obtaining full regulatory approval.

Since every new drug has to go through a unique series of clinical trials before being approved, the process represents a significant risk. The estimated average cost of developing a single new treatment is $1.3 billion. For the vast majority of treatments that never make it to market, this is money thrown out the window.

Biopharmaceutical companies have spent decades looking for ways to optimize the early stages of the discovery process and prioritize treatments that are likely to meet regulatory approval. There is a real need—in both the market and public health spheres—to reliably qualify novel treatments in ways that are both cost- and time-efficient.

Artificial intelligence has the power to help biopharmaceutical companies accelerate how quickly they can discover new drugs and bring them to market to help patients. Biopharmaceutical laboratories are beginning to apply this powerful approach to scientific computing for drug treatment R&D, and they’re delivering promising results.

How AI Leads the Way in Drug Research Acceleration

The very first AI-powered biopharmaceutical discovery occurred in 2007 when a University of Cambridge-built algorithm named Adam identified the function of a yeast gene solely by analyzing public databases. Now, the field’s most advanced technologies are capable of pushing AI-based drug discovery into completely new territory, promising significant efficiencies over existing methods.

Flashing forward to 2020, NVIDIA has announced a 5-petaflops AI system, NVIDIA DGX A100, the world’s most advanced AI system. It harnesses the power of an entire data center into a single platform, offering biopharmaceutical researchers access to end-to-end machine learning capabilities.

This technology is already being used in some of the world’s most advanced scientific computing environments. Leveraging this kind of technology allows researchers to find patterns in enormous quantities of data and build more realistic simulations for early drug discovery and qualification. NVIDIA’s DGX A100 offers the agility and performance of a supercomputer at a fraction the size and cost of competing solutions.

Making AI-Powered Scientific Computing Accessible

Early AI data centers might include over 600 CPU and GPU systems for AI – an entire data center of infrastructure, consuming over 600 kilowatts of power, potentially costing over $11 million in infrastructure.

Today, a state-of-the-art AI data center could achieve the same performance with just five NVIDIA DGX A100 systems in a single rack. This would draw 28 kilowatts of power, cost only $1 million, and significantly reduce the carbon footprint of the resulting research environment.

More than a next-generation offering from a reputable manufacturer, this kind of progress is a game-changing technological advancement that enables completely new use cases for AI technology, especially in the world of biopharmaceutical research and development.

The ability to implement the latest AI-powered drug discovery workflows at one-tenth the cost and one-twentieth the power load is going to be transformative for the biopharmaceutical industry. Drug treatments of the future are going to be more sophisticated—and more accessible—than ever.

Implement Artificial Intelligence in Your R&D Technology Infrastructure

The development of integrated, end-to-end AI systems like the NVIDIA DGX A100 will reshape the drug discovery process for many biopharmaceutical researchers. Analytic processes that once took weeks or months can now be completed in hours, with operating costs reduced to mere fractions of what they were as early as a year ago thanks to incredible advances in machine learning.

But turning data into insight and simplifying some of the world’s most complex workflows will require a new kind of scientific computing expertise. Successfully integrating such a powerful system into a working laboratory demands a competent, experienced service vendor with a confidence-inspiring track record with AI equipment implementations.

Data scientists, biotech IT teams, and pharmaceutical executives need to find and hire reputable support partners before implementing state-of-the-art AI systems in their R&D cycle. Your strategic advisor needs to be able to guide your R&D department through the process with clarity, delivering value in key areas not often found within the core competencies of a biotech research environment:

1. Review and Adjust System Architecture

There is no guarantee that your existing system architecture can handle next-generation AI-driven analytics workflows. Even if it can, it’s easy to overlook critical inefficiencies when deploying new technology unless a system architecture audit is performed.

Investing in an expert partner to examine your scientific computing architecture can help identify potential problems before they become obstacles to the life-saving work biopharmaceutical researchers dedicate their lives to.

2. Analyze Gaps and Mitigate Risks

Asking the right people the right questions can help uncover important gaps in an organization’s ability to respond to the stresses of technology implementation. The gap analysis process can help your research laboratory better understand its strategic goals and structural weaknesses that may stand in the way of achieving them.

When performed by a reputable AI support vendor, this step can be crucial in uncovering challenging obstacles to best-in-class data analysis and scientific computing performance before they become intractable problems.

Professional gap analysis can not only uncover systemic issues to resolve before implementing new technology, but it can also help identify the needs of power-users and stakeholders. This information can prove invaluable during the later stages of implementation.

3. Benefit From Expert Recommendations

Learning from the successes of others can be as valuable as learning from their mistakes—and the world of artificially intelligent scientific computing applications is full of both. Expert support vendors who have worked with research and development teams in the biotech industry bring a valuable perspective to the table.

Being able to tell what approaches work, what approaches don’t, and how different decisions reflect the overall value of the implementation process is critical to long-term success. Make the most of your artificial intelligence deployment by leveraging the knowledge that only experts can offer.

4. Deploy Whole-of-Life Solutions

Implementing new technology immediately brings up a wide range of important and expensive considerations. Having the support of dedicated experts can make it easier to onboard new systems, even those that have been designed with ease-of-use in mind, and get them running quickly to support critical research.

The whole-of-life implementation approach doesn’t stop with deployment. It includes support, maintenance, and continuity. All of these are vital for biomedical research laboratories under pressure to demonstrate the value of the resources invested in their work. A single misstep in long-term application planning can compromise the value of your state-of-the-art AI system.

Approaching the implementation from a whole-of-life perspective mitigates these risks. When support, maintenance, and continuity are included in the plan from the very start, it becomes far less likely that unforeseen challenges will show up and threaten the process in the future.

5. Create a System of Accountability

Accountability is a major consideration that needs to be a part of the AI-driven scientific computing implementation process from the start. Decide who has ownership over decisions made during deployment and who shoulders responsibility for their outcomes.

Choosing to work with an experienced AI hardware vendor will ensure that the weight of this responsibility consistently falls on the most capable and experienced leaders. A partner who has seen transformative implementation projects through to their completion before will be better-informed than a highly qualified but ultimately inexperienced team leader.

Entrust Your Drug Treatment R&D Computing Applications to Reputable Managed Service Vendors

The ability to leverage the collective expertise of a team of AI implementation experts does more than secure a positive outcome for your upcoming deployment. It also ensures that your IT and research team members are able to continue doing what they do best: creating and qualifying life-saving drug treatments for the public.

When R&D laboratories call on AI implementation and support experts for their technology deployments, they significantly improve the efficiencies and outcomes of their technology adoption strategy.

Emerging Tech In the Life Sciences — What You Need to Know

The Life Sciences and Healthcare industries have long been driven by a commitment to innovation. The introduction and advancement of technologies over the past 50 years has increased the pace at which science moves forward in unimaginable ways. Now, emerging technologies like Artificial Intelligence (aka, “AI”), among others, present even greater opportunities to push the limits of discovery.

But for many IT teams, understanding when, where, and how these capabilities best fit within the Bio-IT ecosystem can be both challenging and frustrating. More importantly, distinguishing between the viability of these tools today and the promise they offer for tomorrow can be the deciding factor in whether or not the capabilities will actually add value to your R&D computing workflows.

What is AI?

Artificial Intelligence refers to the use of automated algorithms to perform tasks that traditionally rely on human intelligence. From the voice-controlled Siri or Alexa to self-driving cars AI is growing rapidly. AI can be portrayed as Robots with human-like intelligence or emotions, and it can embrace anything from Google search algorithms to Facebook’s facial recognition needs.

Though the long-term goal of those working to advance the tech is to create an AI that would outperform humans at nearly every intellectual task, Artificial Intelligence today can perform only relatively light-weight tasks such as data analysis, internet searches, facial recognition, and self-driving cars.

Artificial Intelligence in Life Sciences and Healthcare

Over the last few years, the use of Artificial Intelligence has redefined how scientists address a disease, develop new drugs and improve patient outcomes. Computers are able to perform analysis and uncover new datasets at an infinitely quicker pace than human analysis. As a result, new and effective drugs can be made available sooner, and diseases previously deemed too difficult to take on are now gaining the attention of more research teams.

Some ways AI is currently being used effectively within the Life Sciences and Healthcare spaces include:

The Big Question: Is AI Right for Your R&D Goals?

Before you can answer that question, you have to start by asking yourself the following:

It’s also important to be able to explain why traditional methods have failed until now and why AI is necessary for you to succeed. At RCH, we’ve supported many customers in their effort to incorporate Artificial Intelligence into their projects. Here are a few of the most successful use cases we’ve seen:

The Flip Side of AI:

Despite all of the potential AI promises, there are a number of considerations related to data privacy and ethics associated with the use of AI, particularly as they pertain to direct patient care. Who would be held accountable for machine errors that could lead to mismanagement of care? Would patients be informed of the extent of role AI is playing in their treatment? Would AI encourage patients to not seek advice from the medical practitioner and indulge in self-diagnosis and medication? Could the health practitioner be threatened by AI about a potential loss in authority and autonomy? While these are just a few of the questions still swirling around the use of AI, the larger theme is this:

AI is a buzzword and while it does offer some incredibly differentiating potential, it is important to understand what it actually helps to avoid a potentially expensive investment in a technology that won’t add the value you need it to.

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