What are Foundation Models in Biology and Healthcare?

Content:

• History of AI in the Context of Foundation Models
• The Technology Stack Behind Foundation Models
• The Paradigm Shift in Biomedical Research and Drug Discovery
• Challenges of Foundation Models in Biotech and Drug Discovery
• The Future of Foundation Models in Biology

Foundation models are a category of AI models trained on massive amounts of unlabeled data, enabling them to handle a wide variety of tasks, such as text translation or medical image analysis. This is in contrast to earlier AI models, which were specifically trained for one task. Foundation models, often built on transformer architectures and large language models (LLMs), can be fine-tuned for numerous applications with minimal additional data or effort.

The concept was formalized by a 2021 paper from Stanford researchers. These models have demonstrated "impressive behavior" and new features that continue to emerge as researchers explore them further. The idea of emergence refers to discovering hidden or nascent abilities within these models, while homogenization describes the blending of different AI techniques to create more generalized models.

Foundation models and related generative AI technologies (which include transformers, diffusion models, etc.) have captivated attention due to their ability to generate text, images, music, and even software, representing a transformative potential for economic value.

History of AI in the Context of Foundation Models

The evolution of foundation models in AI can be seen as the culmination of several decades of advancements in artificial intelligence, machine learning, and computational sciences. These models, capable of performing a wide range of tasks through pre-training on large datasets and fine-tuning for specific applications, represent a key shift in how AI is developed and deployed. Understanding their significance requires examining the history of AI, particularly through the lens of major milestones that have led to the development of such versatile and scalable models.

Early Foundations of AI (1950s–1980s)

The idea of creating machines capable of intelligence dates back to the mid-20th century. In 1956, the term artificial intelligence was coined during a conference at Dartmouth College. Early AI research focused on symbolic reasoning, problem-solving, and rule-based systems — approaches that tried to emulate human logic and decision-making. One of the key developments in this era was expert systems, which used if-then rules to replicate decision-making processes in specific domains like medical diagnosis or chess. These systems were limited to handling problems that could be explicitly defined by rules and logic.

However, early AI lacked flexibility, scalability, and the ability to learn from vast amounts of unstructured data—traits that foundation models would later excel at.

The Emergence of Machine Learning (1980s–2000s)

In the 1980s and 1990s, the limitations of rule-based systems became evident, and researchers began shifting their focus toward machine learning (ML), a subfield of AI that enables computers to learn from data without being explicitly programmed. Early ML algorithms like decision trees, support vector machines (SVMs), and Bayesian networks allowed for pattern recognition and predictions based on historical data.

By the early 2000s, neural networks began to re-emerge (having been introduced in the 1950s but stagnating due to the "AI winter"). These networks, inspired by the structure of the human brain, were capable of learning from data by adjusting weights through backpropagation. The breakthrough came with the development of deep learning, which used multi-layered neural networks to learn complex representations from large datasets. This was a key moment, as deep learning laid the groundwork for the future development of large models capable of understanding and generating human-like responses.

The ImageNet Challenge in 2012 was a defining moment for deep learning when a convolutional neural network (CNN) developed by Geoffrey Hinton and his team achieved record-breaking performance in image recognition tasks. This success demonstrated the power of large-scale neural networks trained on massive datasets, a precursor to foundation models.

The Rise of Transformers and Large Language Models (2017)

A major leap toward foundation models occurred in 2017 with the introduction of transformer architecture in the paper "Attention Is All You Need" by Vaswani et al. This architecture revolutionized natural language processing (NLP) by introducing the self-attention mechanism, which allows models to process input data in parallel rather than sequentially. This marked a significant departure from earlier models like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, which struggled with long-range dependencies in sequential data.

Transformers enabled models to handle much larger datasets and learn more complex patterns, paving the way for large language models (LLMs) like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). These models could now be pre-trained on vast amounts of unstructured text data and fine-tuned for specific tasks, such as translation, summarization, or even creative tasks like generating essays or code.

BERT (introduced by Google in 2018) became a significant innovation, using bidirectional training to better understand the context of words in a sentence. Following that, GPT-2 and GPT-3 by OpenAI set new benchmarks for generating human-like text, with GPT-3 boasting 175 billion parameters, marking the dawn of massive pre-trained models capable of generalizing across many tasks.

The Advent of Foundation Models (2020s and Beyond)

With the success of transformer-based models like GPT, the idea of foundation models began to take shape. The term was popularized in a 2021 paper by Stanford's Center for Research on Foundation Models, which highlighted the potential of these large, pre-trained models to serve as a base for many downstream applications​

The Technology Stack Behind Foundation Models

The technological backbone of foundation models is a carefully layered architecture that enables them to handle vast datasets, generalize across multiple domains, and solve tasks ranging from natural language processing (NLP) to computer vision. Central to the efficiency and versatility of these models is the integration of several key technologies, including neural networks, transformers, self-supervised learning, and computational infrastructure capable of managing the immense demands of training these large models.

Neural Network Architecture

Foundation models are built on the principles of deep learning, which involves neural networks with multiple layers that progressively learn more abstract features from input data. These models typically consist of feed-forward neural networks, where data passes through the network in one direction, and backpropagation algorithms that adjust weights during training to minimize error.

For example, ResNet and VGG are neural network architectures commonly used as building blocks in models focused on computer vision. These models can be pre-trained on large image datasets like ImageNet, and their feature extraction layers can be adapted into foundation models for specific tasks like image segmentation, object detection, and image captioning.

Transformers: The Core Innovation

The breakthrough that catalyzed the modern foundation model revolution was the introduction of transformer architectures, first described in the influential paper “Attention is All You Need” (Vaswani et al., 2017). Transformers solved many limitations of previous models like RNNs and CNNs by employing self-attention mechanisms, allowing the models to weigh the importance of different parts of the input sequence. This was a game-changer for sequential data tasks like NLP, where understanding the context of words in relation to one another is crucial​.

Transformers are at the core of models like GPT-3, BERT, and DALL-E. Their self-attention mechanism enables them to process entire sequences in parallel, making them much more efficient than previous architectures that required data to be processed in a step-by-step fashion. Bidirectional transformers like BERT even enhance this by reading data both forward and backward in a sequence, which greatly improves the model's comprehension of context and relationships in text​.

Self-Supervised Learning: Automating the Learning Process

Traditional machine learning models required labeled datasets for training, where each input needed an associated output to guide the learning process. Foundation models, however, utilize self-supervised learning, where they learn from unlabeled datasets by discovering patterns and structures in the data. For instance, masked language modeling, used in models like BERT, requires the model to predict missing words in a sentence based on surrounding context. This drastically reduces the need for costly, time-consuming data labeling processes​.

Another variant, next-sentence prediction, helps models understand larger textual contexts by predicting if two sentences logically follow each other. These techniques are key to training foundation models because they allow the AI to learn from raw, unlabeled data—making it possible to pre-train models on massive datasets like entire text corpora, code repositories, or collections of images​.

Computational Power: Scaling for Success

Training foundation models involves processing vast datasets and calculating billions or even trillions of parameters. The computational requirements for such tasks are immense, often involving the use of high-performance GPUs and distributed computing. For instance, GPT-3 was trained using 10,000 GPUs, consuming an enormous amount of energy and computing resources.

To handle the sheer size of these models, cloud infrastructure and parallel processing techniques are indispensable. Large-scale AI companies and research institutions rely on cloud services to scale their model training efforts, using distributed systems that split the workload across many machines. Frameworks like NVIDIA's Megatron help manage the training of massive models such as MT-NLG (Megatron-Turing Natural Language Generation), which boasts over 500 billion parameters. These systems optimize memory and computational efficiency, making it possible to train such large models in a reasonable timeframe).

Fine-Tuning and Customization

After a foundation model is pre-trained on a broad dataset, it can be fine-tuned for specific tasks. This step typically requires far less data and computing power than the initial training. Fine-tuning adjusts the model's weights based on new, task-specific data. For example, a foundation model pre-trained on millions of medical images might be fine-tuned to diagnose a particular disease, using only a small dataset of labeled images.

This ability to fine-tune foundation models for domain-specific tasks is one of their most powerful features, as it reduces the barriers to applying AI across industries. By using transfer learning, organizations can leverage pre-trained models and adapt them to their unique needs without having to start the training process from scratch.

The Paradigm Shift in Biomedical Research and Drug Discovery

The application of foundation models marks a significant paradigm shift in biomedical research and drug discovery. Traditionally, drug discovery followed a linear, target-specific approach, where researchers would focus on identifying a single biological target (such as a protein) and then design or screen for ligands (drug candidates) that could interact with that target. This one-target-one-ligand methodology was highly reductionist, limited by its focus on isolated components of biological systems rather than understanding the complexity and interconnectedness of these systems. The process involved multiple, time-consuming steps: hypothesis generation, target validation, compound screening, lead optimization, and clinical trials. Each step was typically handled separately, often requiring years or decades of iteration.

The introduction of foundation models and advanced AI in biomedical research fundamentally transforms this process by adopting a holistic, systems-based approach. Foundation models—trained on vast amounts of multi-omics data (genomics, transcriptomics, proteomics, etc.), imaging, clinical trials, and biological pathways—can analyze and generate insights from this data in an interconnected manner. Instead of focusing on one biological target, these models integrate various layers of biological complexity, leading to more comprehensive and precise predictions.

Key Shifts in Approach

1. Multi-Omics Integration: Foundation models enable the integration of genomic, transcriptomic, proteomic, and even metabolomic data, allowing researchers to understand disease mechanisms at a systems level. Instead of isolating a single target, foundation models can analyze the interaction of multiple biological networks. This is especially useful for diseases like cancer and neurodegenerative disorders, where multiple genes and pathways are involved. Deep Genomics, for instance, uses AI to map how genetic mutations influence RNA splicing across the genome. This multi-omics approach enables them to identify RNA-based therapeutic targets that would be overlooked by traditional methods​.
2. Data-Driven Target Discovery: Rather than starting with a hypothesis about a specific target, foundation models can mine vast biological datasets to identify patterns and connections that point to novel targets. 
3. Multi-Target Approaches: Diseases, especially chronic and complex ones like cancer and Alzheimer's, are often driven by multiple biological pathways. Foundation models allow researchers to move from a one-target-one-ligand approach to a multi-target strategy. By understanding the systems biology behind disease progression, foundation models can design drugs that target multiple proteins or pathways simultaneously, leading to polypharmacology (the design of drugs that affect multiple targets.
4. Generative Models for Drug Design: Generative AI models, such as those used by Insilico Medicine, can design novel drug candidates by generating entirely new molecules that are optimized for binding to multiple targets. Chemistry42, for instance, is capable of generating new chemical structures while simultaneously considering how these compounds will interact within the entire biological system rather than with a single target​.
5. Personalized Medicine: Foundation models also allow for the integration of personalized patient data, such as genetic information and individual health records, to predict how different patients will respond to various therapies. This is crucial for the development of precision medicine, where treatments are tailored to the specific genetic and molecular profile of a patient. Companies like Bioptimus use multi-omics data to predict drug efficacy and optimize treatment plans for individual patients, marking a shift towards a more personalized, data-driven healthcare model​.
6. Reduction in Experimental Burden: The ability of foundation models to predict outcomes reduces the need for large-scale experimental trials in early discovery phases. By simulating drug-target interactions, toxicities, and pharmacokinetics in silico, these models reduce the number of physical experiments required, accelerating the discovery process and reducing costs. Tools like DiffDock from NVIDIA's BioNeMo platform can predict how drugs dock with protein targets in a virtual environment, allowing researchers to test hundreds of candidates before moving to in vitro or in vivo studies​.

Challenges of Foundation Models in Biotech and Drug Discovery

Foundation models, while revolutionary, are not without challenges, particularly when applied to highly specialized fields like biomedical research and drug discovery. These models, which leverage vast datasets and complex neural networks to generalize across multiple domains, require considerable computational resources, ethical oversight, and careful tuning to deliver meaningful outcomes. In fields as critical and high-stakes as healthcare, the hurdles involved in effectively deploying foundation models are both technical and ethical.

Data Requirements and Accessibility

One of the primary challenges for foundation models is their reliance on vast amounts of data for pre-training. Unlike domain-specific models that can be trained on smaller, curated datasets, foundation models require large-scale, unlabeled datasets to learn generalized patterns before being fine-tuned for specific applications. In healthcare, this poses several issues:

• Access to high-quality data is often limited due to privacy regulations such as HIPAA in the U.S. and GDPR in Europe. Biomedical data, especially genomic and patient health data, are highly sensitive, and obtaining sufficient volumes to train foundation models is difficult.
• Even when data is available, ensuring its quality is another issue. Poorly curated or biased data can lead to inaccuracies in the model's predictions, which can have dire consequences in a healthcare setting. Additionally, datasets in biomedical research often come from a limited number of clinical trials, which may not represent the full diversity of patient populations.

The reliance on domain-specific data is another challenge. Many foundation models, especially those used in biomedical research, need access to highly specialized datasets such as genomic sequences or protein structures. These datasets are expensive to acquire, and in many cases, proprietary, making it difficult for smaller companies or research institutions to leverage foundation models.

Computational and Energy Costs

Training large foundation models is computationally expensive and energy-intensive. Models like GPT-3, which has 175 billion parameters, required over 10,000 GPUs and several weeks to train. The hardware infrastructure required to train these models is expensive to acquire and maintain, often making it impractical for smaller companies or research groups.

This brings with it a broader concern regarding the environmental impact of AI development. The energy consumption associated with training foundation models contributes to a significant carbon footprint. Studies comparing the energy usage of training large AI models to the lifetime emissions of cars have raised concerns about the sustainability of current AI research practices.

Model Interpretability and Explainability

A major challenge in deploying foundation models in healthcare and drug discovery is the lack of interpretability. Foundation models, particularly deep neural networks, are often described as black boxes—while they can make accurate predictions, understanding how they arrive at those predictions is incredibly difficult. This is especially concerning in biomedical applications, where regulatory agencies like the FDA demand that AI models used in clinical settings be explainable and transparent.

For example, if a foundation model recommends a specific treatment plan for a patient based on genomic data, clinicians need to understand the rationale behind the model's recommendation. Without transparency, it becomes difficult to trust the model’s outputs, making it harder to integrate these systems into critical decision-making processes in healthcare​.

Bias and Ethical Concerns

Another significant issue with foundation models is bias. Because these models are trained on vast datasets, they are susceptible to inheriting the biases present in that data. In the biomedical context, this could mean that certain populations are underrepresented in the training data, leading to models that perform worse for these groups. This is particularly problematic in healthcare, where equitable access to effective treatments is essential.

• Biased datasets in biomedical research can lead to AI models that are less effective at diagnosing or treating diseases in certain demographics, exacerbating health disparities. For instance, many clinical trials historically underrepresent minority populations, which could result in foundation models that are less accurate for these groups.
• Another issue is the potential for hallucination, particularly with large language models like GPT-3, which can generate plausible but incorrect information. In a medical context, where accuracy is critical, this can be dangerous. Misinformation could lead to incorrect diagnoses or recommendations, posing risks to patient safety​.

Specialization vs. Generalization

Foundation models are designed to generalize across many tasks, but this generalization can come at the expense of specialization. In drug discovery, for instance, models need to be finely tuned to predict how specific molecules interact with biological targets. While foundation models are good at handling general tasks (e.g., predicting molecular structure), they often require significant fine-tuning to perform well in highly specialized tasks like protein folding or drug-target interaction modeling.

This raises questions about the trade-off between generalization and specialization. While foundation models provide flexibility and can be applied across various fields, this often requires additional data and computational resources to adapt the model to a specialized task. For example, a model trained to understand general biological processes may need extensive retraining to work effectively in cancer biology.

Ethical and Legal Challenges

Beyond technical hurdles, foundation models face a host of ethical and legal challenges:

• Patient privacy is a top concern in healthcare. Using sensitive patient data to train models requires robust data governance frameworks to ensure compliance with privacy laws like GDPR and HIPAA. Companies must also ensure that the data is stored securely and used responsibly, which adds another layer of complexity to implementing foundation models in healthcare settings.
• Intellectual property (IP) is another concern. Many foundation models are trained on publicly available data, leading to questions about ownership of the insights derived from these models. In drug discovery, for example, a company might discover a new compound using a foundation model trained on publicly available genomic data. This raises legal questions about who owns the rights to that discovery.

Deployment and Adaptability

Finally, deploying foundation models in real-world settings poses significant challenges. These models must be adapted to local contexts, particularly in healthcare, where medical practices, regulations, and patient demographics vary across regions. Foundation models trained in one region may need substantial fine-tuning to be effective in another, requiring further investment in infrastructure and data collection.

Moreover, infrastructure needs are considerable. To implement foundation models at scale, organizations need access to cloud infrastructure, data storage, and high-performance computing capabilities. For smaller organizations or startups, the cost of this infrastructure can be a major barrier to entry.

The Future of Foundation Models in Biology

The future of foundation models in AI is set to expand across industries, with transformative impacts on healthcare, drug discovery, and personalized medicine. These models, trained on vast and diverse datasets, offer unprecedented flexibility and scalability, allowing for applications ranging from natural language processing to biological data analysis. 

In drug discovery, foundation models can simulate molecular interactions and predict drug efficacy at unprecedented speeds, while in genomics, they enable multi-omics integration for a more holistic understanding of diseases. As computational power increases and ethical frameworks evolve, foundation models will likely become foundational tools in fields like personalized medicine, precision healthcare, and complex disease modeling