Section 1: Introduction – A New Paradigm for Scientific Inquiry
Scientific inquiry is undergoing a transformation of a magnitude not seen since the advent of computational simulation. This shift is driven by the emergence of foundation models (FMs), a class of artificial intelligence that represents a new paradigm for discovery. These models, defined as large-scale deep learning neural networks pretrained on vast, generalized datasets, possess an unprecedented ability to be adapted to a wide range of downstream tasks.1 Their adaptability, a product of self-supervised learning on immense corpora of unlabeled data, distinguishes them from prior generations of AI and positions them as a pivotal technology for accelerating scientific progress.1 This report provides an exhaustive analysis of the application, impact, and future trajectory of foundation models across three of the most critical and complex domains of modern science: protein folding, materials science, and drug discovery. It will examine the architectural underpinnings of these models, their tangible successes, the cross-cutting challenges that temper their deployment, and the strategic imperatives for researchers, corporations, and policymakers seeking to harness this revolutionary capability.
career-path—chief-data-scientist-By Uplatz
1.1 Defining Foundation Models in the Context of Science
At their core, foundation models are general-purpose systems that can be fine-tuned for specialized applications, providing a powerful alternative to developing bespoke AI models from scratch.1 While generalist FMs such as GPT-4 are trained on the broad expanse of human language and imagery from the internet, scientific foundation models are trained on the distinct and highly structured “languages of science”.6 These languages are multimodal, encompassing vast datasets of molecular structures, protein sequences, genomic data, physical simulations, and the entire corpus of scientific literature. By pretraining on this specialized data, FMs develop a foundational “understanding” of the fundamental patterns, principles, and relationships within specific scientific domains, enabling them to perform tasks ranging from prediction to generation with remarkable accuracy.
The architectural pillar supporting most modern foundation models is the Transformer, a neural network design that excels at tracking long-range dependencies in sequential data.2 This ability to contextualize information across long sequences is what allows FMs to process not only natural language but also the sequential representations of molecules (e.g., SMILES strings) and the primary amino acid sequences of proteins.7 This architectural consistency provides a unified framework for tackling a diverse array of scientific problems.
To contextualize the current landscape of scientific FMs, the following table provides a comparative overview of the key models that will be analyzed in detail throughout this report. It serves as a strategic summary, highlighting the domain focus, core architecture, key innovations, and demonstrated impact of these flagship projects.
Table 1: Comparative Analysis of Key Foundation Models in Scientific Discovery.
| Model Name | Primary Domain(s) | Core Architecture | Key Innovation / Capability | Demonstrated Impact / Scale |
| AlphaFold 3 | Structural Biology, Drug Discovery | Transformer (Evoformer, Pairformer), Diffusion Model | Predicts 3D structures of multi-molecular complexes (protein, DNA, RNA, ligands) with high accuracy. | Solved the protein folding problem; >200M structures predicted; 50% accuracy improvement in interaction prediction over previous methods.11 |
| GNoME | Materials Science | Graph Neural Network (GNN), Active Learning | High-throughput discovery of stable inorganic crystalline materials. | Predicted 2.2 million new crystals (~800 years of knowledge); 380,000 stable candidates released; validated by autonomous labs.14 |
| MatterGen | Materials Science | Diffusion Model | De novo, property-guided generation of novel inorganic materials. | Orders of magnitude more efficient than traditional screening; can design materials based on desired properties.15 |
| JMP (FAIR) | Materials Science, Chemistry | Graph Neural Network (GemNet-OC) | Multi-domain supervised pretraining across diverse chemical datasets for universal property prediction. | State-of-the-art performance on 34/40 benchmark tasks, showing strong generalization to low-data regimes.16 |
| BioNeMo (NVIDIA) | Drug Discovery, Biology | Multimodal (Transformers, GNNs) | A unified, cloud-based platform for training and deploying FMs for molecules, proteins, and cells. | Enables end-to-end in silico drug discovery pipelines, from target ID to lead optimization, for pharmaceutical partners.10 |
| MolE | Drug Discovery | Transformer (DeBERTa adapted for graphs) | Two-step pretraining (self-supervised on structure, multi-task on biology) for molecular property prediction. | State-of-the-art on 9/22 ADMET benchmark tasks in Therapeutic Data Commons.19 |
1.2 The Foundational Shift: Contrasting FMs with Traditional Methods
The rise of foundation models constitutes a significant departure from established scientific methodologies. Traditional computational science has been dominated by domain-agnostic, mathematically grounded algorithms like the Finite Element Method (FEM) and Finite Volume Method (FVM).20 These powerful techniques solve systems of physics-based equations and are celebrated for their universality, interpretability, and rigor. They are, however, fundamentally equation-driven, not data-driven, and serve as a crucial benchmark for the level of general applicability that any new “foundational” tool must strive to achieve.20
Prior to FMs, AI in science was characterized by task-specific machine learning. This involved training smaller, specialized models on narrowly defined, labeled datasets to perform a single function, such as predicting one specific material property or classifying a particular type of medical image.3 These models lacked generalizability; a model trained to predict band gaps could not be repurposed to predict formation energies without starting from scratch. They were brittle, required significant human effort for data labeling, and could not easily adapt to new problems.
Foundation models invert this paradigm. Through massive pretraining, they acquire a broad, transferable understanding of a domain. This allows them to generalize across a wide variety of tasks with minimal task-specific fine-tuning, a process known as transfer learning.1 This approach fundamentally alters the scientific workflow. Instead of a slow, iterative process of formulating a hypothesis and then testing it to weed out what does not work, FMs enable a generative approach: scientists can define a set of desired properties, and the model can directly generate candidate solutions—be they molecules, materials, or proteins—that meet those criteria.6 This shifts the scientific process from one of elimination to one of direct creation.
A critical implication of this paradigm shift is the relocation of the primary bottleneck in scientific research. Historically, the limiting factor in many fields of computational science was the immense computational cost and time required for simulations, such as those based on Density Functional Theory (DFT).6 A single complex calculation could take months on a supercomputer. Foundation models, once trained, can often perform a similar prediction in seconds.6 This extraordinary acceleration does not eliminate bottlenecks but rather moves them. The new primary constraints are the availability of massive, high-quality, standardized datasets required for pretraining, and the development of robust methodologies for validating the outputs of these complex, often opaque, models.20 The central question for scientific progress is evolving from “Can we compute this?” to “Do we have the right data to train a model to learn this?” and, crucially, “Can we trust the model’s output to be physically valid?”
1.3 The Unifying Role of Graph-Based Representations in Scientific FMs
Many of the most complex and high-value domains in science are fundamentally concerned with interconnected systems of entities and their relationships. In biology, this manifests as networks of interacting proteins, gene regulatory pathways, and the intricate dance between drugs and their targets.28 In materials science and chemistry, it is the graph of atoms connected by chemical bonds that defines a molecule or crystal and dictates its properties.29 This inherent network structure of scientific data makes graph-based data models a uniquely suitable framework.
Graph databases, which natively store data as nodes (entities) and edges (relationships), provide an ideal substrate for this scientific knowledge.30 Unlike traditional relational databases, which store data in rigid tables and require computationally expensive “join” operations to reconstruct relationships, graph databases store these connections as first-class citizens.33 This architecture allows for extremely efficient traversal of complex, multi-step relationships—such as tracing a metabolic pathway or identifying all compounds related to a specific biological target—making them a high-performance foundation for the vast knowledge corpora that FMs must navigate.
If graph databases are the substrate, then Graph Neural Networks (GNNs) are the engine. GNNs are a class of deep learning models explicitly designed to operate on graph-structured data.37 They learn by passing messages between connected nodes, allowing them to generate representations (embeddings) that capture not only the features of an individual entity but also the topology of its local neighborhood and its position within the broader network.29 This capability is indispensable for scientific FMs that need to reason about molecular and material structures, where the arrangement of atoms is as important as the atoms themselves.
This recognition leads to a more nuanced understanding of the challenge facing scientific FMs. The “language” they must learn is not merely sequential, like human text, but is inherently multimodal and graph-structured. Scientific information is conveyed through a combination of 1D sequences (DNA, protein primary structure), 2D graphs (molecular connectivity), 3D geometries (protein folds, crystal lattices), textual descriptions (scientific literature), and numerical data (property measurements).40 A truly foundational model for science must be able to ingest, process, and integrate all these modalities. This implies that architectures like GNNs and multimodal transformers are not just useful tools but are fundamental prerequisites for building the next generation of models capable of holistic scientific reasoning.
Section 2: Deciphering the Blueprints of Life – Foundation Models in Protein and Molecular Structure Prediction
The three-dimensional structure of a protein dictates its function, and for half a century, determining this structure was one of the grand challenges of biology. The recent success of foundation models in this domain, spearheaded by DeepMind’s AlphaFold, has not only provided a solution to this long-standing problem but has also fundamentally reshaped the landscape of structural biology and opened new frontiers in biological engineering. This section provides a detailed analysis of the architectural innovations of AlphaFold, its transformative impact, and the subsequent pivot from predicting existing structures to designing novel proteins de novo.
2.1 The AlphaFold Epoch: Architectural Innovations from AlphaFold 2 to AlphaFold 3
The release of AlphaFold 2 in 2020 marked a watershed moment. Its performance in the 14th Critical Assessment of protein Structure Prediction (CASP14) competition was a dramatic leap over all prior methods, achieving a median Global Distance Test (GDT_TS) score of 92.4, where 100 represents a perfect match with experimental structures.9 For many single-domain proteins, its accuracy is comparable to that of laborious experimental techniques like X-ray crystallography, effectively solving what is known as the “protein structure prediction problem”.9
The success of AlphaFold 2 stems from its novel, end-to-end deep learning architecture. It processes two primary streams of information: the amino acid sequence of the target protein and a Multiple Sequence Alignment (MSA) of related sequences from other organisms.9 The MSA provides crucial evolutionary information; if two amino acids consistently mutate in tandem across different species, they are likely to be in close physical contact in the folded protein. The model’s core is a dual-transformer network. One transformer-based module, the “Evoformer,” processes the MSA and a representation of residue pairs, allowing the model to explicitly reason about spatial and evolutionary relationships. This module uses an attention mechanism to capture long-range dependencies, a key feature for modeling the complex global folds of proteins.9 The output of this trunk is then fed into a structure module, which is also based on a transformer design, to generate the final 3D coordinates of the protein’s atoms.9 This entire system is differentiable, meaning it can be trained as a single, integrated unit to optimize the accuracy of the final structure.
The announcement of AlphaFold 3 in May 2024 signaled the next major evolution in the field: a shift from predicting the structure of isolated proteins to predicting the structure of complex biomolecular assemblies.11 This is a critical advance because biological function rarely arises from a single protein in isolation. Instead, it emerges from intricate interactions between proteins and other molecules, including DNA, RNA, small-molecule ligands (which include most drugs), and ions.12 AlphaFold 3 demonstrates a minimum 50% improvement in accuracy for predicting these interactions compared to previous methods, and in some key categories, the accuracy has doubled.11
This enhanced capability is enabled by significant architectural advancements. While retaining the core principles of its predecessor, AlphaFold 3 introduces a simplified and more powerful module called the “Pairformer,” which refines predictions of residue-pair relationships.11 Most notably, it incorporates a diffusion model for generating the final 3D structure.11 This generative approach starts with a diffuse “cloud” of atoms and iteratively refines their positions in 3D space until they converge on the most likely structure, guided by the predictions from the Pairformer module. This diffusion-based generation of atomic coordinates is a fundamental departure from AlphaFold 2’s structure module and is key to its ability to model the complex geometries of multi-molecular interactions.
2.2 Impact Analysis: Transforming Structural Biology and Beyond
The impact of AlphaFold extends far beyond an academic achievement; it has profoundly and irrevocably altered the practice of biological research. The most immediate effect has been the democratization of structural information. In collaboration with the European Molecular Biology Laboratory (EMBL-EBI), DeepMind launched the AlphaFold Protein Structure Database, which provides free, open access to over 200 million high-quality predicted structures, covering nearly every cataloged protein known to science.11 Before AlphaFold, obtaining a single protein structure could take years of expert labor and hundreds of thousands of dollars in experimental costs. Now, this information is available instantly to any researcher with an internet connection. This has saved, by some estimates, hundreds of millions of years of collective research time.13
This vast repository of structural data is accelerating experimental research across the board. AlphaFold models are now routinely used as a starting point for experimental structure determination techniques like X-ray crystallography and cryo-electron microscopy, where an accurate initial model can dramatically speed up the process of interpreting experimental data.9 More broadly, having an immediate structural hypothesis for any protein of interest allows biologists to design more targeted experiments to probe its function.
This accessibility has catalyzed new research avenues in nearly every field of biology. Scientists are using AlphaFold structures to develop new vaccines against malaria, understand the molecular mechanisms of Parkinson’s disease, design enzymes to break down plastic pollution, and combat antibiotic-resistant bacteria.13 The availability of a high-quality predicted structure for virtually any protein has inverted the traditional discovery workflow. Historically, genomics provided vast amounts of sequence data, but linking sequence to function was a slow process bottlenecked by experimental structure determination. Now, researchers can move rapidly from a gene sequence to a reliable predicted 3D structure, and from that structure, formulate concrete, testable hypotheses about the protein’s function. This has effectively created a new field of “structural genomics,” where proteome-wide structural analysis becomes computationally tractable, enabling system-level biological insights that were previously impossible.
2.3 Beyond Prediction: AI-driven De Novo Protein Design
While AlphaFold addresses the “forward problem” of predicting structure from a given sequence, an even more ambitious frontier has emerged: the “inverse folding problem.” This involves designing a completely novel amino acid sequence that will reliably fold into a specific, predetermined 3D shape to carry out a desired function.46 This is the domain of
de novo protein design, and it is here that generative foundation models are enabling a transition from the discovery of natural biology to the engineering of new biology.
Software tools like ProteinMPNN, developed at the University of Washington’s Institute for Protein Design, exemplify this new capability. ProteinMPNN is a deep learning model that can generate viable amino acid sequences for a given structural backbone in seconds—a task that is over 200 times faster and yields more successful designs than previous computational methods.46 This process can be conceptualized in two stages. First, a generative AI can “hallucinate” novel protein folds that do not exist in nature, akin to how an image generation model creates novel images from a prompt. Then, a tool like ProteinMPNN can “inpaint” this structural scaffold with an amino acid sequence that is computationally predicted to fold into that exact shape.
Crucially, this is not merely a theoretical exercise. Proteins designed using these AI tools have been successfully created and validated in the laboratory. Researchers have demonstrated that these de novo proteins fold into their intended shapes with high fidelity and can even self-assemble into complex, functional architectures like nanoscale rings.46 Such structures could serve as building blocks for custom nanomachines, molecular cages for drug delivery, or highly specific biosensors. This success marks a pivotal moment in biology. The goal is no longer limited to understanding the proteins that evolution has produced; it is now possible to engineer entirely new proteins from first principles to solve specific human challenges in medicine, energy, and materials technology.
2.4 Limitations and Frontiers in Structural Biology AI
Despite the revolutionary success of models like AlphaFold, significant challenges remain. AlphaFold’s accuracy, while groundbreaking, is not perfect; for roughly a third of its predictions, the quality is insufficient for high-resolution applications.11 The models also struggle with certain classes of proteins, particularly intrinsically disordered proteins that lack a stable 3D structure, and they have a limited ability to predict the multiple different conformations that a single protein can adopt to perform its function.11
Furthermore, it is critical to distinguish between the “structure prediction problem” and the “protein folding problem.” AlphaFold excels at the former—predicting the final, stable folded state. It does not, however, reveal the physical pathway or dynamics of the folding process itself—how the linear chain of amino acids navigates a vast conformational space to find its functional shape.11 Understanding these dynamics is the next frontier and will be essential for tackling diseases caused by protein misfolding, such as Alzheimer’s and Parkinson’s. The current models primarily provide a static snapshot of a protein. The future of the field lies in developing FMs that can predict the dynamic behavior of proteins and their interactions within the complex, crowded, and fluctuating environment of a living cell, thereby providing a more complete picture of biological function.
Section 3: Engineering Matter from First Principles – Foundation Models in Materials Science
The chemical space of possible materials is astronomically vast, far exceeding what can be explored through traditional experimental or computational methods. The development of new materials with tailored properties—for applications ranging from clean energy and sustainable plastics to next-generation electronics—has historically been a slow, expensive, and serendipitous process. Foundation models are now poised to revolutionize this field by enabling a paradigm shift from Edisonian trial-and-error to data-driven, in silico design. This section examines how FMs are being deployed for both the prediction of properties for known materials and the generative discovery of entirely novel ones.
3.1 Predictive Power: FMs for Accurate Material Property Prediction
A central task in materials informatics is predicting a material’s properties (e.g., conductivity, hardness, stability) from its atomic structure. A major obstacle has been the scarcity and high cost of obtaining this data, whether through physical experiments or computationally intensive quantum mechanical simulations like Density Functional Theory (DFT).17 This data scarcity has traditionally limited the performance and generalizability of machine learning models.
Foundation models directly address this challenge by learning universal representations from broad, diverse datasets that can then be fine-tuned for specific property prediction tasks.17 By pretraining on large databases of known materials, these models capture fundamental relationships between structure and properties that are transferable across different chemical systems. This approach significantly improves predictive accuracy, especially in low-data regimes where only a few examples of a specific property are available for fine-tuning.
The architectures for these models are heavily influenced by the nature of materials data. Many FMs for property prediction adapt encoder-only transformer architectures, like BERT, to process string-based representations of molecules (SMILES) or crystal structures.48 However, Graph Neural Networks (GNNs) have emerged as a particularly powerful and natural choice, as they can directly operate on the graph structure of molecules (atoms as nodes, bonds as edges), allowing them to learn from the material’s topology.16 State-of-the-art models such as GemNet-OC and MACE-MP-0, which are GNN-based, have set new benchmarks for accuracy in property prediction.16
A landmark success in this area is the Joint Multi-domain Pretraining (JMP) model developed by Meta’s FAIR team. This approach involves pretraining a single GNN on a diverse collection of datasets spanning different chemical domains—including small molecules, large molecules, and crystalline solids—and different tasks, such as predicting energies and atomic forces.16 When this pretrained model was fine-tuned on specific downstream tasks, it achieved new state-of-the-art performance on 34 out of 40 different benchmark tests. This result provides compelling evidence that a single foundation model can indeed learn a generalizable “understanding” of chemistry that is broadly applicable across disparate domains.
3.2 Generative Design: The Discovery of Novel Materials
The traditional approach to computational materials discovery has been high-throughput virtual screening, which involves calculating the properties for every material in a large, pre-existing database. This is an exhaustive and inefficient process. Generative foundation models are flipping this paradigm on its head. Instead of screening what already exists, they directly generate novel material structures that are predicted to have specific, desirable properties.7
The most spectacular demonstration of this capability is the Graph Networks for Materials Exploration (GNoME) project from Google DeepMind.14 Using a GNN in an active learning loop with DFT calculations, GNoME explored the space of stable inorganic crystals at an unprecedented scale. The project predicted 2.2 million new crystal structures, a number equivalent to nearly 800 years of prior knowledge. Of these, 380,000 are considered stable enough to be synthesizable in a laboratory, dramatically expanding the catalog of known viable materials. This discovery includes 52,000 new layered compounds similar to graphene and 528 potential lithium-ion conductors, which could fuel advances in electronics and battery technology.14
While GNoME excels at generating a vast number of stable materials, other models are focused on conditional generation—designing materials that meet specific property requirements from the outset. Microsoft’s MatterGen, for example, is a diffusion model that can be prompted with a desired property, such as a target band gap for a semiconductor, and will generate novel crystal structures predicted to exhibit that property.15 This property-guided approach is vastly more efficient than generating millions of random structures and then screening them for the desired trait.
This generative-predictive cycle mimics an accelerated version of the scientific method. A generative model like MatterGen or GNoME acts as the “hypothesis engine,” proposing vast numbers of novel material structures. Then, a predictive model serves as the “virtual experiment,” rapidly assessing the stability and properties of these generated candidates. The results of these predictions can then be fed back to refine the generative model in a continuous, self-improving loop.14 This automated cycle of hypothesis and validation operates at a speed and scale that is simply unachievable through human-led research.
A key enabler of this cycle is the development of universal Machine Learning Interatomic Potentials (MLIPs). Models like M3GNet, MACE-MP-0, and Microsoft’s MatterSim function as general-purpose “physics engines” for atomic simulations.15 Trained on massive DFT datasets covering a wide swath of the periodic table, these FMs can predict the forces between atoms for a vast range of chemical systems, effectively replacing the need for expensive, system-specific DFT calculations in many molecular dynamics simulations. This democratizes access to high-fidelity simulation, allowing researchers to quickly and cheaply validate the stability and dynamic behavior of AI-generated materials.
3.3 Multimodality as a Cornerstone for Holistic Representation
A material cannot be fully described by its atomic structure alone. Its identity and properties are captured across a range of data modalities: 1D string representations like SMILES, 2D molecular graphs, 3D geometries, textual descriptions in scientific literature, and various forms of spectral data, such as the electronic Density of States (DOS).40 A truly comprehensive foundation model for materials science must be able to understand and translate between these different “languages.”
To this end, researchers are developing multimodal foundation models that learn a shared, unified latent space where these different representations can be connected. Models like MultiMat from UC Berkeley and IBM’s Mixture-of-Experts (MoE) model are pioneering this approach.7 By learning to align the embeddings from a crystal structure encoder, a DOS encoder, and a text encoder, for example, MultiMat can perform powerful cross-modal tasks. It could, in theory, take a textual description of a desired property (e.g., “a transparent, conductive oxide”) and generate a list of candidate crystal structures, or take a crystal structure and predict its corresponding electronic spectrum. This holistic integration of information is crucial for building models that can reason about materials in a way that more closely mirrors the multifaceted approach of a human scientist.
3.4 Challenges in Materials AI: Physical Consistency and Synthesizability
Despite the rapid progress, two fundamental challenges loom over the field of AI for materials discovery. The first is ensuring physical consistency. Unlike language or images, where generated outputs are judged by human perception, materials must obey the inviolable laws of physics, such as conservation of energy and principles of symmetry.4 A standard generative model trained on data may learn statistical correlations but has no inherent understanding of these physical laws, and can thus generate structures that are plausible but physically impossible. Overcoming this requires the development of physics-informed AI, where physical constraints are explicitly incorporated into the model’s architecture or training process to guide it toward valid solutions.
The second, and perhaps greater, challenge is the synthesizability gap. A model can generate a crystal structure that is computationally predicted to be perfectly stable but which cannot be created using any known laboratory synthesis techniques. This gap between theoretical stability and practical synthesizability is a major hurdle. Closing this loop requires training models on data that includes successful and failed synthesis recipes, or developing secondary FMs that can predict the likelihood of a given material being synthesizable. A crucial step in this direction is the A-Lab project at Lawrence Berkeley National Laboratory, which successfully used GNoME’s predictions to program a robotic lab that autonomously synthesized 41 novel materials, demonstrating a closed loop from AI prediction to physical realization.14 This integration of AI with automated experimentation represents the future of the field, ensuring that computational discovery translates into real-world materials.
Section 4: Accelerating Therapeutics – Foundation Models Across the Drug Discovery Pipeline
The pharmaceutical industry is characterized by exceptionally long development timelines, staggering costs, and an astonishingly high failure rate, with approximately 90% of drug candidates failing in clinical trials.51 Foundation models offer the potential to radically reshape this landscape by introducing unprecedented speed, accuracy, and insight at every stage of the drug discovery and development pipeline. From identifying novel biological targets to designing bespoke molecules and predicting their clinical efficacy, FMs are being deployed to tackle the core challenges that have long plagued therapeutic development.
4.1 Target Identification and Validation
The first step in developing a new drug is to identify a biological target—typically a protein—whose modulation could alleviate a disease. This process has traditionally been slow and reliant on piecing together fragmented evidence from years of research. Foundation models are dramatically accelerating this stage by their ability to synthesize knowledge from the vast and exponentially growing corpus of biomedical data.23
These models can process and integrate heterogeneous data at a massive scale, including genomic and proteomic data, electronic health records, clinical trial results, and the full text of millions of scientific publications. By applying deep learning to these datasets, FMs can uncover novel, statistically significant correlations between genes, proteins, and diseases that might be missed by human researchers. Generative AI platforms, such as Insilico Medicine’s PandaOmics, leverage these capabilities to not only identify potential targets but also to generate and rank novel therapeutic hypotheses.18 For example, by analyzing complex biological pathways and gene regulatory networks, the system can propose a previously unconsidered protein as a viable target for a specific cancer. This AI-driven approach was instrumental in the discovery of a novel drug candidate for idiopathic pulmonary fibrosis, which progressed from target discovery to the initiation of Phase II clinical trials in under 30 months—a process that traditionally takes many years.53
4.2 De Novo Drug Design: Creating Novel Molecules
Once a target is identified, the next challenge is to find a molecule that can interact with it in a desirable way (e.g., inhibiting an overactive enzyme). Foundation models are enabling a move away from screening existing chemical libraries toward de novo drug design: the creation of entirely new molecules computationally, tailored to the specific target.56
A variety of generative architectures are being employed for this task, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and, increasingly, diffusion models, which have shown great promise in generating realistic 3D molecular structures.59 These models can operate in two main modes. In ligand-based design, the model learns the features of known active compounds and generates novel molecules with similar properties. In structure-based design, the model is given the 3D structure of the target’s binding pocket (often predicted by a model like AlphaFold) and generates molecules that are geometrically and chemically complementary to that site.57 This latter approach is particularly powerful for tackling novel or historically “undruggable” targets that lack known binders.51
The most sophisticated approaches are multimodal, integrating different molecular representations to create a more holistic design process. Platforms like NVIDIA’s BioNeMo and IBM’s biomedical foundation models can process molecular graphs, 1D SMILES strings, and 3D conformations simultaneously.10 This allows them to generate a molecule and then immediately predict its 3D structure and how it will bind to its target, creating a tight, iterative design loop that bridges the gap between chemical structure and biological function.
This ability of FMs to explore the vastness of “chemical space” (estimated at 10^60 possible drug-like molecules) represents a fundamental change in the discovery process.56 Traditional methods have only ever explored a tiny fraction of this space. Generative FMs can invent entirely new chemical scaffolds and molecular architectures that a human chemist might never conceive. While they may not generate a “perfect” drug on the first try, they vastly expand the searchable territory for novel therapeutics. Predictive FMs then act as intelligent guides, highlighting the most promising regions of this newly expanded space for experimental exploration, thereby fundamentally improving the odds of success.23
4.3 Predicting Interactions and Efficacy
Generating a novel molecule is only part of the challenge; it is equally critical to predict whether it will be effective and safe. A major cause of late-stage drug failure is poor ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties. Foundation models are being deployed as powerful predictive engines to weed out unpromising candidates early in the process, saving immense time and resources.
Models like MolE are pretrained on large databases of chemical structures and their known properties. This pretraining allows the model to learn a rich representation of molecular features. It can then be efficiently fine-tuned on smaller, specific datasets to predict a wide array of properties, from binding affinity to a target (Drug-Target Interaction or DTI prediction) to toxicity or metabolic stability.19 By running generated molecules through a gauntlet of these predictive models, researchers can prioritize candidates that not only bind to their target but are also likely to be well-behaved in the human body.
This integration of generative and predictive capabilities is collapsing the traditionally linear and siloed stages of drug discovery into a unified, iterative, and data-centric workflow. The old pipeline of Target ID -> Hit Generation -> Lead Optimization -> Preclinical Testing, where each step was a distinct and lengthy phase, is being replaced.53 Now, a target can be identified, a set of novel molecules generated for it, and their key properties predicted within a rapid,
in silico “design-build-test-learn” cycle. A molecule with a predicted toxicity issue can be immediately fed back to the generative model with new constraints for redesign. This allows for the exploration of thousands of design variants computationally before committing to the expensive and time-consuming process of chemical synthesis and laboratory testing.
4.4 Case Studies: Analysis of Leading Biomedical FMs
Several major technology and research organizations are developing comprehensive platforms to harness FMs for drug discovery, each with a distinct strategy.
- IBM’s Biomedical Foundation Models (BMFMs): IBM Research is developing a suite of FMs that target different stages of the discovery pipeline. These include models for target discovery from omics data, models for designing biologics (like antibodies), and models for small-molecule drug design.41 A key aspect of their strategy is multimodality—training models on multiple molecular representations simultaneously—and a commitment to open-sourcing many of their models and tools to foster community innovation.
- NVIDIA’s BioNeMo: This is a cloud-based platform and service designed to provide the pharmaceutical industry with access to state-of-the-art FMs without requiring them to build the massive infrastructure themselves.10 BioNeMo offers a suite of models that operate across biological scales, from the molecular level (MolMIM for generative chemistry) to the protein level (e.g., OpenFold for structure prediction) and the cellular level (e.g., Phenom-Beta for analyzing cell images). This creates a unified ecosystem for building end-to-end computational drug discovery workflows.
- MolE (Molecular Foundation Model): This model showcases a specific architectural innovation for property prediction. It adapts the DeBERTa transformer architecture, which has been highly successful in NLP, to operate on molecular graphs.19 Its two-step pretraining strategy—first learning chemical structures through self-supervision, then learning biological information through massive multi-task training—has enabled it to achieve state-of-the-art performance on a wide range of ADMET prediction benchmarks, demonstrating the power of sophisticated pretraining strategies tailored to chemical data.
Section 5: Cross-Cutting Challenges and Strategic Imperatives
While foundation models promise to catalyze a new era of scientific discovery, their development and deployment are fraught with significant challenges that cut across all scientific domains. These hurdles are not merely technical but also involve fundamental questions about data, trust, and the responsible application of powerful, opaque technologies. Overcoming these obstacles is a strategic imperative for the entire research enterprise, requiring concerted effort from academia, industry, and policymakers.
5.1 The Data Bottleneck: Quality, Quantity, and Standardization
The adage “garbage in, garbage out” is amplified to an enormous scale for foundation models. Their performance is fundamentally contingent on the quality and quantity of their training data, and in science, this presents a formidable bottleneck. Unlike natural language processing, where internet-scale text data is readily available, high-quality scientific data is often scarce, expensive, and difficult to obtain.20 Generating a single data point may require a lengthy laboratory experiment or a costly supercomputer simulation.
Furthermore, scientific data lacks the inherent standardization of text or pixels. Data generated in different laboratories, using different equipment or under slightly different conditions, can contain subtle but significant “batch effects” that can confound a model’s learning process.20 Datasets are often siloed in proprietary corporate databases or formatted in bespoke ways, making the aggregation of large, diverse training corpora a major data engineering challenge.
This data landscape is also plagued by noise and bias. Experimental measurements have inherent uncertainties, and existing databases are often heavily skewed toward well-studied systems—for example, kinase proteins in drug discovery or common alloys in materials science.26 A model trained on such biased data will inherit these biases, limiting its ability to generalize to novel, unexplored areas of scientific space, which is precisely where the greatest potential for discovery lies.
5.2 The “Black Box” Problem: Interpretability and Scientific Validity
A core tension exists between the statistical nature of foundation models and the deterministic, causal nature of scientific laws. FMs are powerful correlational engines; they learn complex patterns from data but do not inherently understand the underlying physical principles governing those patterns.2 This leads to the “black box” problem: a model may make a highly accurate prediction, but it cannot explain
why it made that prediction in the language of science.25 In scientific research, the reasoning behind a conclusion is often as valuable as the conclusion itself, as it leads to new understanding and testable hypotheses.
A critical manifestation of this problem is the risk of generating physically nonsensical results. A generative model for materials, for instance, might produce a crystal structure that violates fundamental laws of thermodynamics or symmetry unless it is explicitly constrained.4 Current methods for enforcing such constraints, often by adding penalty terms to the model’s loss function, can be seen as imperfect patches on a fundamentally statistical architecture. The next major breakthrough in scientific FMs may require novel architectures that can natively represent and reason with symbolic physical laws, creating a true synthesis of data-driven learning and first-principles modeling.
Compounding this issue is the problem of “hallucination,” where models generate outputs that are plausible-sounding but factually incorrect.23 In a consumer chatbot, a hallucination might be an inconvenience; in drug discovery or materials design, it could lead to millions of dollars in wasted experiments. This necessitates the development of rigorous validation frameworks and uncertainty quantification methods to establish trust in model outputs.
5.3 Generalization vs. Specialization
The very definition of a “foundation” model rests on its ability to generalize across a wide range of downstream tasks. However, the extent to which current scientific FMs achieve this is a subject of intense debate and investigation. Recent studies have revealed significant limitations in the “zero-shot” performance of some models—that is, their ability to perform tasks for which they have not been specifically fine-tuned.64 In some cases, these large, complex models have been shown to perform no better, or even worse, than simpler, traditional computational methods or even a randomly initialized version of the model itself.
This raises critical questions about what these models are actually learning. Their impressive performance on benchmark tasks may sometimes be an artifact of the fine-tuning process on data that is very similar to the training set, rather than evidence of a deep, foundational understanding. This is particularly concerning in fields like genomics, where some FMs have been shown to be insensitive to single-nucleotide variations—the very changes that can be the difference between health and a genetic disease.67 This suggests that the models are learning broad patterns but failing to capture the fine-grained details that are scientifically and clinically crucial.
This “Wild West” phase of scientific FM development, where numerous models are being published with claims of foundational capabilities but without standardized, rigorous evaluation, is likely to lead to a “crisis of reproducibility”.20 It becomes impossible for the community to meaningfully compare different models or to trust their reported performance. This situation mirrors the early stages of development in other machine learning fields and will almost certainly necessitate a concerted community effort to establish robust, multi-domain benchmarks—analogous to ImageNet for computer vision or the GLUE benchmark for NLP—along with open, standardized datasets. Only through such rigorous and transparent evaluation can the field mature from a collection of bespoke research projects to a reliable ecosystem of foundational tools.68
5.4 Computational and Resource Overheads
The sheer scale of foundation models presents a formidable barrier to entry. Training a state-of-the-art FM from scratch requires computational resources that are available to only a handful of the world’s largest technology companies and government-funded research consortia. The process can consume thousands of high-end GPUs running continuously for months, costing tens of millions of dollars in hardware and energy.1
This concentration of resources risks creating a new “digital divide” in science, where access to the most powerful research tools is limited to an elite few, rolling back the trend toward the democratization of scientific computation. Even for those not training models from scratch, the cost and complexity of deploying and fine-tuning a massive pretrained model can be prohibitive for smaller academic labs or startups.5 Addressing this challenge will require not only innovations in model efficiency but also a commitment to building shared public infrastructure and ensuring broad access to these transformative technologies.
Section 6: The Future Horizon – From AI-Assisted to AI-Driven Discovery
The integration of foundation models into the scientific enterprise is not a static endpoint but a dynamic, evolving process. The trajectory of this evolution points toward a future where AI transitions from a passive tool for analysis to an active participant in the discovery process, and ultimately, to an autonomous agent capable of conducting science itself. Understanding this trajectory is essential for navigating the profound opportunities and risks that lie ahead and for strategically shaping the future of scientific inquiry.
6.1 A Framework for Progression: The Three Stages of Integration
The evolving role of FMs in science can be understood through a three-stage framework, which describes a progressive increase in their autonomy and collaborative capacity.24
- Stage 1: Meta-Scientific Integration. This is the current, predominant state of affairs. In this stage, FMs function as powerful assistants that enhance and automate discrete tasks within existing scientific workflows. They are used for accelerated literature review, high-throughput data analysis and processing, code generation for simulations, and interpretation of complex experimental results. Here, the FM is a tool that makes the human scientist more efficient, but it does not fundamentally alter the structure of the research process.
- Stage 2: Hybrid Human-AI Co-Creation. This stage, which is now beginning to emerge, sees FMs evolving into active collaborators in the creative aspects of science. Models are used to generate novel hypotheses by identifying non-obvious patterns in data, to help design complex experiments, and to engage in a reasoning process with the human researcher. Systems like Coscientist, which can plan and execute chemical experiments based on high-level goals, are early examples of this paradigm.69 In this hybrid model, discovery is a product of the synergy between human intuition and expertise and the AI’s ability to process information at a massive scale.
- Stage 3: Autonomous Scientific Discovery. This is the long-term vision, where FMs operate as self-directed scientific agents. Such an agent would be capable of identifying important open questions, formulating hypotheses, designing and executing virtual or physical experiments to test them, analyzing the results, and iterating on its understanding with minimal human intervention.24 This represents a paradigm shift from AI-assisted science to AI-driven science, where the AI is no longer just a tool but an engine of discovery in its own right.
6.2 The Prospect of Autonomous Scientific Agents
The leap from collaborative tool to autonomous agent is not merely a matter of increasing the size or accuracy of the core foundation model. Current FMs are largely static systems that map an input to an output.1 True scientific discovery, however, is an interactive, dynamic process that involves probing the world—whether by querying a database, running a new simulation, or controlling a laboratory instrument.24
Therefore, the path to autonomy lies in the development of “agentic” AI systems. This involves building a framework around a powerful FM “brain” that provides it with the “hands and eyes” to act in the world.66 Such an agent must have the ability to use external tools: to access calculators for precise computation, to query knowledge bases and databases for factual grounding, to run simulation software, and, most critically, to interface with robotic hardware in “self-driving labs” that can physically synthesize the materials or perform the biological experiments designed by the AI. The A-Lab’s autonomous synthesis of GNoME-predicted materials is a landmark proof-of-concept for this closed-loop integration.14
The AlphaEvolve project offers another tantalizing glimpse of this future. By creating an evolutionary ecosystem of LLMs that could directly modify code, evaluate the results, and iteratively select for improvements, the system autonomously discovered novel, provably correct algorithms that surpassed human-designed solutions in mathematics and computer science.69 This demonstrates that in a purely digital domain, autonomous discovery is already within reach.
However, the prospect of autonomous scientific agents raises profound epistemic and ethical questions. If an AI makes a discovery, who gets the credit? How can we validate and trust findings generated by an opaque, autonomous system? And what is the role of human creativity, intuition, and skepticism in a world where the pace of discovery is dictated by machines? The potential for amplifying biases or for misuse in developing harmful materials or bioweapons is immense, demanding proactive development of robust safety and governance frameworks.24
6.3 Recommendations for R&D Strategy
Navigating this complex and rapidly evolving landscape requires a coordinated and strategic approach from all stakeholders in the scientific ecosystem.
- For Research Institutions: The primary imperative is to foster deep, interdisciplinary collaboration. Progress will not come from AI experts or domain scientists working in isolation, but from their integrated efforts.68 Institutions must invest in the difficult, unglamorous work of creating large-scale, high-quality, standardized, and open datasets, which are the essential fuel for these models. Furthermore, they should lead the charge in developing community-wide, rigorous benchmarks to ensure that progress is real and reproducible.
- For Corporations: The strategic advantage will go to those who move beyond using AI for isolated tasks and instead invest in building or adapting FMs as a core, unifying platform for their R&D efforts. A key focus should be on creating a “data flywheel,” where the results of every physical experiment are structured and fed back to continuously improve the underlying models.7 Strategic partnerships with academic institutions to tap into cutting-edge research and with major tech companies to access computational scale will be critical.
- For Policymakers and Funding Agencies: The immense cost of developing FMs from scratch risks concentrating this powerful technology in the hands of a few large corporations. To ensure a competitive and equitable research landscape, public funding should be directed toward creating shared national and international computational and data infrastructure, democratizing access for academic and smaller commercial researchers.25 Supporting high-risk, long-term research into the next generation of AI architectures—particularly those that integrate physical principles and enhance interpretability—is essential for sustained progress.
The development of these models is likely to create a bifurcated ecosystem. A small number of massive, general-purpose, proprietary FMs will be developed by large tech companies, accessible primarily through APIs.3 In parallel, a vibrant and dynamic ecosystem of smaller, more specialized, open-source models will flourish, driven by academia and startups.7 These open models will be fine-tuned on specific, high-quality datasets to tackle niche problems. The interplay between these two tiers—the competition and collaboration between large-scale proprietary models and agile open-source alternatives—will be a primary engine of innovation in the coming decade.
6.4 Concluding Analysis: The Enduring Impact on the Scientific Method
Foundation models are more than just a new set of powerful tools; they are catalysts for a fundamental evolution of the scientific method itself. For centuries, science has operated primarily on a hypothesis-driven model. The rise of FMs is accelerating a shift toward a complementary paradigm that is data-driven, exploratory, and generative. They augment human creativity and intuition with the ability to process information and navigate complexity at a scale and speed that is beyond human cognition. By automating the cycle of hypothesis, experiment, and analysis in the digital realm, they are dramatically accelerating the overall pace of discovery. The integration of these models into the fabric of scientific research is not merely an incremental improvement but a step-change in our ability to understand and engineer the world, heralding a new and transformative chapter in the history of science.