Abstract

Ebola 1.0 is a generative AI model (460M Parameters) introduced by Pathogen AI, designed to predict and simulate future mutations of the Zaire Ebolavirus virion spike glycoprotein—an essential capacity for proactive disease control, vaccine development, and ongoing virological research. This sequence-to-sequence model uses a transformer architecture, wherein the encoder—termed the “Pathogen AI Foundation Model”—is pre-trained on two objective functions to robustly learn the underlying structural and contextual features of viral nucleotide sequences. The decoder employs causal attention—focusing solely on prior tokens—to generate high-fidelity outputs, decoding each nucleotide in a sequential manner.

By focusing on precise nucleotide-level decoding—which allows the model to generate subtle changes that might be overlooked at the amino acid level—Ebola 1.0 provides exceptional accuracy in mutation prediction. Empirical results indicate a 99.823% reconstruction accuracy when tested on time-sorted and de-duplicated virion spike glycoprotein sequences. While greedy decoding, under the conservative mutation rate of the gene (0-2 mutations), the model identifies 87.3% of mutations correctly. The ai model appears to understand how natural evolution forces impact the gene.

The model’s input is a current virion spike glycoprotein sequence for which a user seeks future-state predictions, while its output projects a mutation profile one year into the future. This fine-grained approach allows researchers to anticipate potential viral evolution, informing preventative strategies and therapeutic interventions. Ebola 1.0 demonstrates the feasibility of applying high-resolution, transformer-based generative AI models to the rapid and accurate prediction of viral mutations, paving the way for more proactive public health measures, broader applications to other pathogens, and potential expansions for emerging diseases.

Introduction

Accurate prediction of emerging viral mutations is critical for effective disease surveillance, a deeper understanding of viral transmissibility and pathogenicity, and the development of robust vaccines. When scientists and public health officials have the foresight to detect key molecular changes in viral pathogens, they can implement targeted containment strategies before widespread outbreaks occur. In the case of the Zaire ebolavirus, the virion spike glycoprotein not only plays a pivotal role in viral attachment and entry but also contains critical antigenic domains that facilitate robust neutralizing immune responses, making it the primary target for prophylactic measures. Because of its central function in mediating host-cell invasion, even minor modifications in this glycoprotein can lead to significant shifts in transmission dynamics, disease severity, or immune escape. A model that can reliably estimate how this gene might evolve over time thus provides a valuable edge in staying ahead of the virus’s adaptive changes, ensuring that current vaccine formulations remain effective and future preventive measures can be rapidly deployed.

By anticipating likely future variants of the spike glycoprotein, healthcare providers, vaccine manufacturers, and global health organizations can better allocate resources and develop strategies that prevent or mitigate large-scale outbreaks, while enabling more precise vaccine design to improve overall efficacy and potentially shorten development timelines. Such predictive capabilities also allow policymakers to make informed decisions regarding resource distribution and emergency preparedness, maintaining agility in public health infrastructures as the virus evolves. As a result, the Ebola 1.0 model exemplifies how generative AI can address these urgent challenges, offering a high-resolution perspective on viral evolution that informs interventions designed to save lives and curb epidemics. Its advanced modeling framework generates at the nucleotide level, capturing subtle sequence changes that might escape conventional predictive methods.

Building on this foundation, the model’s architecture employs the pre-trained Pathogen AI Foundation Model as its transformer encoder. This model was trained using two objective functions to ensure it effectively captures both the structural and contextual nuances of viral genomes, enhancing its ability to analyze and predict viral evolution with high accuracy. Through the extensive pre-training of the encoder, Ebola 1.0 attains robust understanding of mutation patterns across a diverse range of viral strains, significantly enhancing its predictive power. Its decoder employs nucleotide-level causal attention to sequentially generate high-fidelity predictions, enabling precise mutation profiling that directly supports the development and refinement of effective vaccines. This targeted approach not only expedites laboratory validation but also facilitates the design of next-generation immunizations aimed at halting the Zaire ebolavirus in its earliest stages.

AI Model Prediction Scoring Perfect Accuracy. Democratic Republic of Congo Zaire Ebolavirus Strain Mayibout Glycoproteine (GP) Gene, Complete CDS. Input sequence date: 2019-04-12. Ground truth sequence date: 2020-04-15. 5 mutations correctly predicted

Performance

The AI model demonstrates strong overall performance in predicting mutations within the Zaire ebolavirus GP gene. The model excels at identifying precise genetic changes in regards to likely natural evolution forces, making it a reliable tool for early-stage viral evolution monitoring. While detection accuracy naturally decreases as the number of mutations increases, the model still captures valuable information, providing meaningful insights into the genetic progression of the virus. This robust capability makes it a powerful asset for researchers studying viral adaptation, and with further refinements, it could become even more effective at identifying high-mutation sequences.

Interpreting the Performance Charts

The bar charts presented in this analysis illustrate the AI model's ability to detect mutations within the Zaire ebolavirus GP gene across varying levels of sequence divergence. The x-axis represents the number of mutations missed by the model (Left most being a perfect prediction), while the y-axis quantifies the percentage of mutations that the model identified. Higher values on the y-axis towards the left indicate superior model performance, as fewer mutations were missed. Higher values on the y-axis towards the right indicate inferior model performance. As the mutation count increases, the model naturally faces greater complexity, which may lead to a gradual increase in the percentage of missed mutations. A consistently low percentage of missed mutations across mutation counts suggests robust mutation recognition capabilities, whereas increasing values at higher mutation counts highlight areas for potential refinement. This analysis serves as a critical evaluation of the model’s utility in genomic surveillance and viral evolution tracking given the context of the evolution per sequence.

0 Mutations in Ground Truth

For sequences without any mutations, the AI model demonstrates near-perfect accuracy, correctly identifying the stability of the sequence. This strong baseline performance confirms that the model is well-calibrated to recognize un-mutated regions, ensuring a high level of confidence when it later detects evolutionary changes. One example the model had predicted 8 mutations.

1 Mutation in Ground Truth

With a single mutation, the model continues to perform exceptionally well, capturing all of these single changes. This indicates that the AI can successfully detect early-stage mutations in the GP gene, which is critical for tracking the virus’s evolution in its initial phases.

2 Mutations in Ground Truth

At two mutations, the AI model remains incredibly strong, detecting most (70%) mutations perfectly. While the challenge naturally increases with more variation, the model still manages to accurately capture the majority of genetic changes, reinforcing its ability to track mutations as they emerge. A handful of examples include extra ai predicted mutations.

3 Mutations in Ground Truth

When the mutation count reaches three, the AI maintains solid performance, identifying many of the mutations. Despite the complexity increasing, the model continues to provide meaningful insights into sequence evolution, demonstrating its reliability even as the mutation rate grows.

4 Mutations in Ground Truth

At four mutations, the AI struggles, the model identifies the mutations in a small amount of samples. Further investigation into why this is happening might reveal changes to be made in the dataset creation process.

5 Mutations in Ground Truth

With five mutations, the AI remains capable of recognizing key sequence changes, though some mutations may become harder to detect. Despite this, the model continues to provide useful insights into the evolving genetic landscape, reinforcing its role in studying viral mutations over time. The model predicts 1 of the mutations pretty confidently and it also hits all others in a few sequences.

6 Mutations in Ground Truth

Even at six mutations, the AI maintains a level of accuracy that allows for meaningful analysis. While more mutations present additional challenges, the model still successfully identifies a couple of them, proving its effectiveness in analyzing sequences undergoing significant genetic drift and unnatural evolution factors.

7 Mutations in Ground Truth

With seven mutations (well beyond the natural mutation rate), the AI continues to contribute valuable information about sequence evolution. Though more mutations mean increased difficulty in detection, the model still retains its ability to pick up on genetic shifts, making it a helpful tool for researchers studying viral progression. The model confidently identifies 2-3 of the mutations consistently. A theory being that these 2-3 mutations are part of the expected evolution while others could be from unnatural evolution forces.

8 Mutations in Ground Truth

Even at the highest mutation count, the AI provides important insights into the genetic landscape of the virus. While highly mutated sequences pose a likely unpredictable challenge, the model still manages to identify a handful of these mutations, showcasing its ability to analyze complex viral evolution patterns.
This analysis highlights the AI model’s strengths in detecting mutations across a wide range of genetic variations. While performance naturally decreases as mutations accumulate, the model remains a powerful tool for studying the evolution of the Zaire ebolavirus GP gene. Further refinements could enhance its ability to capture even the most complex mutations, reinforcing its value in genomic research and viral monitoring.

Architecture

Ebola 1.0 generates a probability distribution at each time step of the likelihood of each token being next.

Ebola 1.0 employs a sequence-to-sequence, encoder-decoder transformer architecture to generate precise nucleotide-level predictions of future viral mutations. The encoder is the pre-trained Pathogen AI Foundation Model, composed of stacked self-attention transformer blocks that efficiently capture structural and contextual patterns in viral genomes. This extensive multi-objective pre-training allows the encoder to remain frozen during training, ensuring that learning updates are concentrated on the decoder and the language modeling head. The model encompasses 460M parameters and is fully updated during training.

The decoder employs causal self-attention for autoregressive generation and cross-attention mechanisms to reference the complete encoder sequence from the final encoder block. The output layer consists of a language modeling head that decodes into four tokens—‘A’, ‘T’, ‘C’, and ‘G’—enabling byte-level decoding. The decoder independently learns its token and position embeddings, enhancing its adaptability to diverse viral sequence patterns. Notably, the decoder is designed to be shallow (6 transformer blocks of 17M Parameters), leveraging the robust representations learned by the encoder for efficient and accurate predictions.

During training, only the decoder and the language model head undergo updates, preserving the pre-trained encoder’s ability to model complex viral sequence representations. For Ebola 1.0, evaluation is performed using greedy decoding, yielding high-fidelity sequence outputs that closely align with observed viral evolution patterns. This architecture achieves a balance between computational efficiency and predictive accuracy, reinforcing its role in vaccine development and viral surveillance.

This targeted approach accelerates laboratory validation and supports the development of next-generation immunizations aimed at preventing the spread of Zaire ebolavirus at its earliest stages. By predicting viral mutations in advance, the model provides crucial insights that seamlessly integrate into vaccine development pipelines, enabling researchers to refine antigen selection, optimize immunogen designs, and prioritize vaccine candidates with the highest efficacy against emerging viral strains.

Model Confidence - Identified Positions of Likely Mutation

Conclusion

Ebola 1.0 constitutes a significant advancement in AI-driven viral mutation forecasting, offering a nuanced understanding of the evolutionary trajectory of the Zaire ebolavirus GP gene. Through its fine-grained nucleotide-level modeling, this transformer-based AI system facilitates anticipatory strategies for vaccine development, disease mitigation, and genomic surveillance. The model exhibits an exceptional 99.823% reconstruction fidelity and accurately predicts 87.3% of mutations under a constrained natural mutation rate (0-2 mutations), underscoring its ability to model evolutionary pressures with high precision. By integrating a robust encoder-decoder transformer framework with extensive pre-training, Ebola 1.0 emerges as an indispensable tool in combatting emerging infectious diseases. Future refinements, encompassing optimized training datasets, architectural enhancements, and sophisticated generative methodologies, will further augment its predictive accuracy, ensuring that public health initiatives maintain a strategic advantage in tracking and mitigating viral evolution.