Drug discovery has long been a complex and time-consuming process.

Traditionally, researchers have relied on a combination of scientific intuition and trial-and-error methods to identify potential drug candidates.

However, a new wave of companies is emerging that utilize artificial intelligence (AI) to streamline this process. Xaira Therapeutics is one such company, and it recently made a significant splash in the industry with a massive $1 billion launch.

Xaira’s ambitious AI drug discovery goal

Xaira isn’t just throwing money at the problem; they’ve assembled a dream team of experts.

The company boasts big names like Dr. Marc Tessier-Lavigne, former president of Stanford University and chief scientific officer at Genentech, at the helm as CEO. Dr. David Baker, a renowned biochemist and director of the Institute for Protein Design at the University of Washington School of Medicine, is also a co-founder.

This team brings not only deep scientific knowledge but also a proven track record in the pharmaceutical industry. Additionally, key researchers who developed powerful AI models for protein and antibody design, RFdiffusion and RFantibody, have joined Xaira from Baker’s lab. This combination of scientific and AI expertise positions Xaira to make significant strides in drug discovery.

What Xaira is doing differently?

The company boasts a unique approach that combines three core areas: machine learning research, data generation, and therapeutic product development.

This three-pronged strategy allows Xaira to build a powerful platform for drug discovery across various treatment modalities. The company believes that by harnessing the capabilities of machine learning, they can unlock a deeper understanding of biological processes. This newfound knowledge can then be used to identify promising drug targets and design potential treatments with greater accuracy and efficiency.

Xaira isn’t shy about tackling challenging targets.

Traditionally, some proteins and biological molecules have proven difficult to manipulate with conventional drug discovery methods. Xaira’s AI platform aims to address this very issue. By analyzing vast datasets and utilizing advanced machine learning algorithms, Xaira hopes to predict how potential drug candidates might interact with these complex targets, paving the way for the development of effective treatments for currently difficult-to-drug diseases.

And the technique being used to do so?

• Machine learning research: The company invests heavily in research to develop new computational methods for biological discovery. These methods will help Xaira not only identify potential drug targets but also understand the underlying biological mechanisms of diseases.
• Data generation: AI thrives on data. Xaira recognizes this and has built robust data generation models to fuel its machine learning algorithms. These models can generate vast amounts of relevant data, allowing Xaira to train its AI with the information it needs to make accurate predictions about potential drugs.
• Therapeutic product development: Developing a potential drug candidate into a safe and effective treatment is a complex process. Xaira has assembled a team of experts in therapeutic product development to bridge the gap between AI discovery and real-world applications. This team will ensure that promising drug candidates identified by AI are rigorously tested and brought to market effectively.

From theory to reality

Xaira isn’t just focused on building a sophisticated AI platform. The company also possesses a robust infrastructure for translating these computational predictions into tangible drug candidates. Their team includes seasoned professionals with expertise in drug development, ensuring a smooth transition from the virtual world of AI simulations to the real-world process of clinical trials and drug manufacturing.

While the promising company is still in its early stages, they have already begun assembling a pipeline of potential drug candidates. These candidates are being evaluated for their potential to treat various diseases, with a particular focus on those that have previously proven challenging for traditional drug discovery methods. The success of these initial ventures will be crucial in demonstrating the effectiveness of Xaira’s AI-powered approach.

The investor consortium backing Xaira is also a testament to the confidence this technology inspires. Alongside the co-leading ARCH Venture Partners and Foresite Labs, prominent names like NEA, Sequoia Capital, and Lux Capital joined the venture, securing Xaira $1 billion in funding.

The bumpy road ahead

The coming years will be critical for Xaira as they navigate the complex stages of clinical trials and the regulatory hurdles of drug approval.

This journey won’t be without its challenges.

The use of AI in medicine has been a topic of debate for a long time. Skeptics question the ability of AI to replicate the nuanced decision-making processes of human doctors, while others raise concerns about potential biases within the algorithms themselves.

Despite these hurdles, the company’s success could pave the way for a new era of faster, more efficient, and more targeted drug discovery. If their AI-powered approach proves effective, it could change the way we develop treatments.

The next few years will be crucial for Xaira in determining the impact of AI on healthcare.

Image credit(s): Emre Çıtak/Freepik Pikaso

• AI-powered drug development can shorten the time it takes to acquire and access information, cutting pharmaceutical development time in half and keeping the cost of new medications under control.
• This is becoming increasingly crucial as the cost of discovering and developing new drugs rises.
• Despite several accomplishments, certain challenges must be overcome before pharmaceutical (pharma) firms may reap the benefits of AI.
• We’ve compiled a list of AI’s most prevalent issues for drug development.

AI can speed up the procedures of gathering and accessing information, cutting medication development time in half and keeping the cost of new medicines in check. This is becoming increasingly important as the cost of identifying and developing medications rises.

Importance of AI-powered development

How much does it cost to produce a new drug? According to a 2020 study, the typical research and development expenditure for a new therapy was $985 million. The high trial failure rate contributed significantly to this expense. Approximately seven out of every eight compounds that reach the clinical testing process never make it to the market.

This is where artificial intelligence comes in. AI can discover previously unknown patterns, resulting in new knowledge of illnesses and the treatments developed to treat them. Astra Zeneca, for example, employs machine learning models to find which genes might produce resistance to cancer therapies more rapidly, while Samsung has developed an app to detect early Covid-19 infection.

Challenges facing AI-powered drug development in the future

Despite the numerous achievements, particular hurdles must be solved for pharmaceutical (pharma) enterprises to gain the benefits of AI. Here, we gathered the most common problems faced by AI-powered drug development.

Smaller datasets available

To learn, most AI systems require large datasets. Creating huge data sets for each type of medical condition is difficult due to the enormous range of diseases and illnesses and the relatively small number of incidents. 

On average, machine learning models in the pharmaceutical sector require 2 to 3 years of historical data to function well. Because of the huge number of mergers and acquisitions, this aim might be difficult to achieve, especially when the original source of the data is no longer available. Because pharma data sets are often smaller, with fewer patients and fewer observations per patient, gaining useful insights is more difficult.

The complexity of the data 

At the same time, while there may be fewer data sets, each dataset may have significantly more features. Patient data may comprise historical and present health or sickness information, treatment history, lifestyle decisions, and genetic data. 

Biometric data, any quantifiable physical feature measured by a sensor or wearable device, can also be included. Consequently, patient data may comprise alphanumeric information, x-rays, pathology reports, and clinical test results in various formats such as JPEG/JPG and digital imaging and communication for medicine (DICOM). Any AI-powered drug development must be able to handle a wide range of complicated data.

Complicated data labeling 

Data labeling is more difficult and needs highly skilled expertise. Consider the many forms of knowledge necessary to recognize the skeletal, internal-organ, nervous system, and vascular abnormalities in X-Ray pictures. Finding the appropriate knowledge is difficult, but labeling may be tiresome and time-consuming. Each brain scan used for cancer screening must be evaluated by doctors (typically three or more), with each inspection taking 5 to 15 minutes.

Data bias

Several groups have a lengthy history of missing or misrepresented in medical databases. Misdiagnoses and tragic results might occur if the training data is not representative of the total population. MLOps protocols and Machine learning (ML) model scoring must be built to monitor and detect drift with a continuous feedback loop for AI ethics and transparency. A diverse ML team should regularly test the models to promote transparency and remove data bias from machine learning models.

Absence of data standards

The industry must establish its own definition of what constitutes a good data set. Organizations may collect and use multiple data types, use different coding methods in their systems, use null or dummy data when needed information is unavailable, and document demographic data inconsistently. Companies are still unsure how to replicate study results without well-defined criteria. Once it is obvious how to construct a legitimate data collection, it may be more easily used by other organizations to study further.

The Medical Field is Changing Because of Artificial Intelligence

Shortage of available biologic data

Biological databases are essential in bioinformatics. They provide scientists with access to a wide range of biologically relevant data, including genomic sequences from an expanding number of organisms. 

There are numerous ways to use biological data, such as comparing sequences to develop a theory about the function of a newly discovered gene, examining known 3D protein structures to discover patterns that can help predict how the protein folds, or investigating how proteins and metabolites in a cell collaborate to make the cell function. AI-powered drug development relies on this data to understand how patients respond to medicinal treatments.

Regulations in the sector

The pharmaceutical sector is heavily regulated, demanding full disclosure and transparency at every stage of the medication development process. This necessity frequently lengthens and increases the cost of AI-powered drug development. Pharma firms should work with authorities to simplify this process for the benefit of everyone. Both regulators and corporations may use AI and other digital transformation projects to increase the value effectiveness of regulatory activities.

Other hurdles that AI-powered drug development face

In addition to these issues, pharma businesses face standard implementation roadblocks. For example, a flexible infrastructure is required to gather, validate, operate applications, perform data governance, and grow. New technologies, such as greater transfer learning and MLOps platforms, can assist train models and streamline the process of transferring machine learning models into production.

New artificial intelligence can diagnose a patient using their speech

The increased competition to identify therapies faster will enhance the need for AI-powered drug development. The problems of evaluating medical data are growing more complicated. Still, the rate of innovation in developing AI tools and technology that will allow pharma firms to unearth smarter insights faster is also increasing.