esm3.esm3.full10

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ESM3 reasons over the sequence, structure, and function of proteins.

This sentence is about a computer program called ESM3. The program is designed to analyze and understand the building blocks of proteins, which are important molecules in our bodies. Proteins have a specific sequence, structure, and function, and ESM3 is able to reason or make logical conclusions about these aspects of proteins. This can be helpful for scientists who want to understand how proteins work and how they can be used to treat diseases.

All three modalities are represented by tokens, and are input and output as separate tracks that are fused into a single latent space within the model.

This sentence is describing a model that uses three different types of data (modalities) and combines them into a single space within the model. The data is input and output separately, but then combined in a way that allows the model to use all three types of data together. The sentence is using technical terms that may be unfamiliar to someone who is not an expert in this field.

ESM3 is trained with a generative masked language modeling objective:

This sentence is related to a type of machine learning called natural language processing (NLP). ESM3 is a model used in NLP that is trained using a specific technique called generative masked language modeling. This means that the model is trained to generate text by predicting the missing words in a sentence, which are represented by "masks". The goal is to improve the model's ability to understand and generate human language.

A random mask $m$ is applied to the tokens $x$ describing the protein, and the model is supervised to predict the identity of the tokens that have been masked.

This sentence is describing a process used in a machine learning model that is trying to understand the structure of proteins. The model is given a set of "tokens" that describe the protein, and a "random mask" is applied to some of these tokens. This means that some of the information about the protein is hidden from the model. The model is then trained to predict what the hidden information is, based on the information that is still visible. This helps the model learn how to understand the structure of proteins even when some information is missing or unclear.

During training, the mask is sampled from a noise schedule so that ESM3 sees many different combinations of masked sequence, structure, and function, and predicts completions of any combination of the modalities from any other.

During the training process, the system randomly selects a "mask" from a set of possible options. This mask can be applied to different aspects of the data, such as the sequence, structure, or function. The system then uses a model called ESM3 to predict the missing information based on the available data. This means that the model is able to make predictions for any combination of the different modalities, even if some of the information is missing or incomplete.

This differs from the classical masked language modeling (28) in that the supervision is applied across all possible masking rates rather than a single fixed masking rate.

This sentence is discussing a technique called masked language modeling, which is used in natural language processing. The classical approach to masked language modeling involves using a fixed masking rate, which means that a certain percentage of words in a sentence are randomly replaced with a special token. However, the approach being discussed in this sentence is different because it applies supervision across all possible masking rates, which means that the model is trained on a wider range of masking rates. This can potentially improve the performance of the model.

This supervision factorizes the probability distribution over all possible predictions of the next token given any combination of previous tokens, ensuring that tokens can be generated in any order from any starting point (29-31).

To generate from ESM3, tokens are iteratively sampled.

This sentence is discussing a technique called "supervision factorization" which is used in a process called "iterative sampling" to generate tokens from a model called ESM3. The technique allows for any combination of previous tokens to be used to predict the next token, and ensures that tokens can be generated in any order from any starting point. Essentially, this means that the model can generate text in a flexible and efficient way.

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Starting from a sequence of all mask tokens, tokens can be sampled one at a time, or in parallel, in any order, until all tokens are fully unmasked (Fig.1A).

This sentence is describing a process where a sequence of tokens (which are like pieces of information) are being revealed or "unmasked" one at a time, or in parallel (meaning at the same time), in any order. The process starts with all the tokens being hidden or "masked", and continues until all the tokens are fully revealed or "unmasked". The figure mentioned (Fig.1A) is likely a visual representation of this process.

Masking is applied independently to sequence, structure, and function tracks, which enables generation from any combination of empty, partial, or complete inputs.

This sentence is talking about a process called "masking" that is used in a specific context. The context is not clear from the sentence alone, but it seems to be related to some kind of data analysis or manipulation.

The sentence says that masking can be applied to three different types of tracks: sequence, structure, and function. This means that whatever masking is, it can be done on different aspects of the data being analyzed.

The sentence also says that masking can be done independently for each type of track. This means that you can apply masking to just one type of track, or to any combination of the three types.

Finally, the sentence says that masking enables the generation of something from any combination of empty, partial, or complete inputs. This means that whatever is being generated (again, it's not clear from the sentence alone) can be created using data that is missing some information (empty), has some but not all information (partial), or has all the information needed (complete).

Overall, this sentence is describing a flexible and adaptable process for analyzing or manipulating data.

ESM3's training objective is also effective for representation learning.

The sentence means that the goal of ESM3's training is not only to improve its performance on a specific task, but also to help it learn how to represent information in a useful way. This is important because good representation learning can lead to better performance on a variety of tasks.

We choose a noise schedule that balances generative capabilities with representation learning (Appendix A.2.2).

This sentence is about a specific approach to training a machine learning model. The "noise schedule" refers to the way in which randomness is introduced into the training process. The goal is to find a balance between allowing the model to generate new data (generative capabilities) and learning from the existing data (representation learning). The details of this approach can be found in Appendix A.2.2.

Tokenization enables efficient reasoning over structure.

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In this sentence, "tokenization" refers to the process of breaking down a larger piece of text (like a sentence or paragraph) into smaller, individual pieces called "tokens". These tokens can be words, phrases, or even individual characters.

The sentence is saying that by breaking down text into these smaller tokens, it becomes easier to analyze and understand the structure of the text. This can be especially useful in fields like natural language processing, where computers are trying to understand and interpret human language.

So, in short, tokenization is a technique that helps computers more efficiently analyze and understand the structure of text.

Protein structures are tokenized by a discrete auto-encoder (32), which is trained to compress the high dimensional space of three-dimensional structure into discrete tokens (Fig.1C).

The sentence is describing a process where a computer program called a "discrete auto-encoder" is used to compress the complex three-dimensional structure of proteins into simpler, more manageable "tokens". This is done by training the program to recognize patterns in the protein structures and then assigning each pattern a unique token. The resulting tokens can then be used to more easily analyze and compare different protein structures.

We propose an invariant geometric attention mechanism to efficiently process three-dimensional structure.

This sentence is about a new method that has been proposed to process 3D structures efficiently. The method is called "invariant geometric attention mechanism" and it is designed to be effective regardless of the specific 3D structure being processed. The idea is to use a type of attention mechanism that is based on the geometry of the 3D structure, which allows for more efficient processing. This method could be useful in a variety of fields, such as computer vision, robotics, and virtual reality.

The mechanism operates in local reference frames defined by the bond geometry at each amino acid, and allows local frames to interact globally through a transformation into the global frame (Appendix A.1.6).

This sentence is describing a process that involves a mechanism that works within specific reference frames, which are determined by the structure of amino acids. These local frames can then interact with each other on a larger scale through a transformation into a global frame. The details of this transformation are explained in Appendix A.1.6.

This mechanism can be efficiently realized through the same computational primitives as attention (33), and is readily scalable.

This sentence is discussing a mechanism that can be easily implemented using the same basic building blocks as a process called "attention" (which is a concept in computer science). The mechanism is also able to handle larger amounts of data without difficulty.

The local structural neighborhoods around each amino acid are encoded into a sequence of discrete tokens, one for each amino acid.

This sentence is describing a process where the structure of a protein is being analyzed. The structure of a protein is made up of amino acids, which are like building blocks. The sentence is saying that the way these amino acids are arranged around each other is being studied. To do this, each amino acid is given a special code or label, which is called a "discrete token". This code helps scientists understand how the amino acids are arranged and how they interact with each other.

When predicting or generating protein structure, structure tokens output by ESM3 are passed to the decoder, which reconstructs the all-atom structure.

This sentence is about a process used in protein structure prediction and generation. ESM3 is a tool that predicts the structure of proteins, and it outputs "structure tokens" which are like building blocks for the protein. These tokens are then passed to a "decoder" which is a program that uses them to reconstruct the full, detailed structure of the protein at the atomic level. So, in simpler terms, ESM3 predicts the structure of a protein and the decoder helps to create a more detailed and accurate picture of that structure.

The autoencoder is trained to encode and reconstruct atomic coordinates with a geometric loss that supervises the pairwise distances and relative orientations of bond vectors and normals (Appendix A.1.7.3.1).

The sentence is describing a process in which a type of machine learning algorithm called an autoencoder is being trained to encode and reconstruct atomic coordinates. This means that the algorithm is being taught to take in data about the positions of atoms and then output a representation of that data in a different format. The algorithm is being trained using a specific type of loss function called a geometric loss, which is designed to ensure that the output of the algorithm accurately reflects the distances and orientations between different atoms. The details of how this loss function works are explained in a separate section of the text, which is referred to as Appendix A.1.7.3.1.

This tokenization delivers nearperfect reconstruction of protein structure ( $<0.3 \AA$ RMSD on CAMEO, Fig.S3), enabling representation of structure at the input and output with atomic accuracy.

This sentence is discussing a process called tokenization, which is used to represent protein structures in a computer program. The sentence states that this tokenization method is very accurate, with a root mean square deviation (RMSD) of less than 0.3 angstroms on a program called CAMEO. This means that the method is able to accurately represent the structure of proteins at a very small scale. The sentence also mentions that this accuracy allows for the representation of protein structures with atomic accuracy, which means that the program can accurately depict the positions of individual atoms within the protein structure.

We also find that providing ESM3 direct access to atomic coordinates in the input via a geometric attention projection into the transformer improves the response to atomic coordinate prompts.

This sentence is discussing a research finding related to a machine learning model called ESM3. The researchers found that by giving the model direct access to atomic coordinates (which are specific locations of atoms in a molecule), they were able to improve the model's ability to respond to prompts related to atomic coordinates. This was achieved by using a technique called geometric attention projection into the transformer, which is a type of neural network architecture used in machine learning. Overall, this finding suggests that providing more specific information to the model can improve its performance in certain tasks.

ESM3 can be conditioned on either or both of tokenized structure and atomic coordinates.

This sentence is discussing a process called "conditioning" in the context of a program or algorithm called "ESM3". Conditioning means that the program can be set up to take into account certain factors or variables when it is running. In this case, the program can be set up to consider either the structure of something (which has been broken down into smaller parts called "tokens"), or the specific coordinates of atoms within that structure. The sentence is saying that the program can be customized to use one or both of these factors when it is running.

We supplement these structure representations with coarse grained tokens encoding secondary structure state (SS8) and solvent accessible surface area (SASA).

This sentence is describing a process where they are adding additional information to their structure representations. The information they are adding includes coarse grained tokens, which are a simplified way of representing certain aspects of the structure. Specifically, they are adding tokens that represent the secondary structure state (SS8) and the solvent accessible surface area (SASA). The secondary structure state refers to the way the protein chain is folded, while the solvent accessible surface area refers to how much of the protein is exposed to the surrounding environment. By adding this information, they can get a more complete picture of the protein structure.

Function is presented to the model in the form of tokenized keyword sets for each position in the sequence.

This sentence is about a model that receives information in a specific format. The information is divided into different parts, and each part is labeled with a set of keywords. These keywords are presented to the model in a way that it can understand and process them. The model is designed to work with this type of input, which is called "tokenized keyword sets".

ESM3 is a bidirectional transformer.

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A transformer is a device that transfers electrical energy from one circuit to another through electromagnetic induction. In the context of electrical engineering, a bidirectional transformer is a type of transformer that can transfer energy in both directions, meaning it can convert electrical energy from one voltage level to another in either direction.

So, when we say that ESM3 is a bidirectional transformer, we're saying that it's a device that can transfer electrical energy in both directions and convert voltage levels as needed.

While extensive research has gone into creating specialized architectures and training objectives for proteins, we find that tokenization paired with a standard masked language modeling objective and the basic transformer architecture is highly effective for both representation learning and generative modeling.

This sentence is discussing the effectiveness of a specific approach to training computer models to understand and generate protein structures. The researchers found that using a simple method called "tokenization" and a common training objective called "masked language modeling" with a basic model called the "transformer" was very effective for both learning how to represent proteins and creating new protein structures. This is surprising because many researchers have focused on creating specialized architectures and training objectives for proteins, but this study shows that a simpler approach can work just as well.

Sequence, structure, and function tracks are input as tokens, which are embedded and fused, then processed through a

This sentence is describing a process where different types of data (sequence, structure, and function tracks) are combined and analyzed together. The data is first broken down into smaller pieces called tokens, which are then combined and processed through a system. This process is called embedding and fusion. The end result is a more comprehensive understanding of the data.

Figure 1. ESM3 is a generative language model that reasons over the sequence, structure, and function of proteins.

This sentence is describing a computer program called ESM3. This program is designed to understand and create new information about proteins, which are important molecules in our bodies. ESM3 uses complex algorithms to analyze the sequence, structure, and function of proteins. It can also generate new information about proteins based on what it has learned.

(A) Iterative sampling with ESM3. Sequence, structure, and function can all be used to prompt the model. At each timestep $\mathrm{t}$, a fraction of the masked positions are sampled until all positions are unmasked.

This sentence is describing a process called "iterative sampling with ESM3." ESM3 is a type of computer model that can be used to predict the structure and function of proteins. The process involves starting with a partially masked protein sequence, which means that some of the amino acid positions are hidden. The model then uses a combination of sequence, structure, and function information to predict what the hidden amino acids might be. This prediction is done in a series of steps, with a small fraction of the masked positions being sampled at each step. The process continues until all of the masked positions have been unmasked and the complete protein sequence has been predicted.

(B) ESM3 architecture. Sequence, structure, and function are represented as tracks of discrete tokens at the input and output. The model is a series of transformer blocks, where all tracks are fused within a single latent space; geometric attention in the first block allows conditioning on atomic coordinates. ESM3 is supervised to predict masked tokens.

The sentence is describing a type of computer model called ESM3, which stands for "Evolutionary Scale Modeling 3". This model is designed to predict missing information in sequences of data, such as DNA or protein sequences.

The model works by representing the sequence, structure, and function of the data as "tracks" of small pieces of information called "tokens". These tokens are used as input and output for the model.

The model itself is made up of a series of "transformer blocks", which are a type of neural network architecture. These blocks are designed to process the input tokens and generate new tokens as output.

One important feature of the ESM3 model is that all of the different tracks of information are combined into a single "latent space". This means that the model can use information from all of the tracks to make predictions, rather than just looking at one type of information at a time.

Finally, the model is "supervised", which means that it is trained on a set of data where the correct answers are already known. This allows the model to learn how to make accurate predictions on new data.

Overall, the ESM3 model is a powerful tool for predicting missing information in complex sequences of data, and has many potential applications in fields like biology and genetics.

(C) Structure tokenization. Local atomic structure around each amino acid is encoded into tokens.

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In this sentence, "structure tokenization" refers to a process of breaking down the local atomic structure around each amino acid into smaller, more manageable parts called "tokens." These tokens can then be used to analyze and understand the structure of the amino acid more easily.

Amino acids are the building blocks of proteins, which are essential for many biological processes in our bodies. By studying the local atomic structure around each amino acid, scientists can gain insights into how proteins are formed and how they function.

Tokenization is a common technique used in many fields, including natural language processing and computer science, to break down complex data into smaller, more manageable parts. In this case, it is being used to analyze the structure of amino acids.

(D) Models are trained at three scales: 1.4B, 7B, and 98B parameters. Negative log likelihood on test set as a function of training FLOPs shows response to conditioning on each of the input tracks, improving with increasing FLOPs.

This sentence is discussing the process of training models at different scales, specifically 1.4B, 7B, and 98B parameters. The sentence also mentions the use of negative log likelihood on a test set to measure the effectiveness of the training. The term "FLOPs" refers to the number of floating-point operations performed during the training process. The sentence notes that the response to conditioning on each of the input tracks improves with increasing FLOPs. In simpler terms, this means that as more computational power is used during training, the models become better at processing and understanding the input data.

(E) Unconditional generations from ESM3 98B (colored by sequence identity to the nearest sequence in the training set), embedded by ESM3, and projected by UMAP alongside randomly sampled sequences from UniProt (in gray). Generations are diverse, high quality, and cover the distribution of natural sequences.

stack of transformer blocks.

This sentence is describing a process used in a computer program called ESM3. The program generates sequences of data, and the sentence is explaining how these sequences are created and analyzed. The program uses a technique called "unconditional generations" to create new sequences, which are then compared to existing sequences in a training set. The sequences are then "embedded" using a technique called ESM3, which helps to organize and group similar sequences together. Finally, the sequences are "projected" using a technique called UMAP, which creates a visual representation of the data. The sentence also mentions that the program uses a "stack of transformer blocks," which is a type of machine learning algorithm used to analyze and generate data. Overall, the sentence is describing a complex process used in a computer program to generate and analyze data.

The first transformer block also includes a geometric attention layer for atomic structure coordinate conditioning.

This sentence is related to a machine learning model called a transformer. The first block of the transformer includes a special layer called a geometric attention layer, which takes into account the coordinates of atomic structures. This layer helps the model to better understand the relationships between different atoms in a molecule or material.

At the output of the model, shallow MLP heads project the final layer representation into token probabilities for each of the tracks.

This sentence is describing a process in a machine learning model. The "output of the model" refers to the final result of the model's calculations. The "final layer representation" is the last step in the model's calculations, which produces a set of numbers that represent the input data in a specific way. The "shallow MLP heads" are a type of machine learning algorithm that takes this final layer representation and uses it to make predictions about the input data. In this case, the predictions are "token probabilities for each of the tracks," which means the algorithm is predicting the likelihood that each input data point belongs to a certain category or "track."

The largest ESM3 model is trained on 2.78 billion natural proteins derived from sequence and structure databases (2, 34-37).

The sentence is about a computer model called ESM3, which is used to study proteins. The model was trained using a large amount of data, specifically 2.78 billion natural proteins that were obtained from databases that contain information about protein sequences and structures. The numbers 2, 34-37 refer to references or sources where more information about the databases can be found.

As a small fraction of structures have been experimentally determined relative to sequences, we leverage predicted structures $(4,5)$.

This sentence is discussing the fact that only a small number of structures have been determined through experiments compared to the number of sequences available. To address this issue, the researchers are using predicted structures as a way to supplement the limited experimental data. The numbers 4 and 5 are likely referring to specific methods or techniques used for predicting structures.

We also generate synthetic sequences with an inverse folding model (described in Appendix A.2.1.3) for all structures, including predicted ones.

This sentence is about generating artificial sequences using a mathematical model. The model is called an "inverse folding model" and it is used to create sequences for all types of structures, including those that have been predicted. The details of the model are explained in a section called Appendix A.2.1.3.

Function keywords are derived by predicting functional annotations from sequence using a library of hidden markov models (38).

This sentence is about a process called "Function keyword prediction" which is used to identify the functions of different parts of a sequence (like DNA or protein). The prediction is made using a library of hidden Markov models, which are mathematical models that can analyze patterns in the sequence data. The sentence is saying that this prediction method is being used to identify functional annotations, which are descriptions of what different parts of the sequence do.

Overall this increased training data to 3.15 billion protein sequences, 236 million protein structures, and 539 million proteins with function annotations, totaling 771 billion unique tokens.

This sentence is describing the amount of data that was used in a study or project. The data includes 3.15 billion protein sequences, 236 million protein structures, and 539 million proteins with function annotations. The total amount of unique tokens, which are individual pieces of data, is 771 billion. This means that the study or project had access to a very large amount of data to work with.

Full details of the training dataset are described in Appendix A.2.1.8.

The sentence is informing the reader that more information about the training dataset can be found in a specific section of the document, which is Appendix A.2.1.8. This section likely contains details about how the dataset was created, what it includes, and how it was used in the research or analysis being discussed.

We train ESM3 models at three scales: 1.4 billion, 7 billion, and 98 billion parameters.

This sentence is about training a type of machine learning model called ESM3 at different scales. The scales are measured in parameters, which are the adjustable values that the model uses to make predictions. The sentence means that the researchers are training ESM3 models with 1.4 billion, 7 billion, and 98 billion parameters. These different scales allow the researchers to compare the performance of the models and determine which scale is best for their specific task.

In an initial series of experiments to evaluate representation learning performance in response to architecture hyperparameters, we find a greater response to increasing depth than to width.

This sentence is discussing a study that was conducted to see how well a computer program can learn and understand information. The researchers tested different ways of organizing the program's structure, called "architecture hyperparameters," to see how it affected the program's ability to learn. They found that increasing the depth of the program's structure had a bigger impact on its learning ability than increasing its width.

This informed the choice of relatively deep networks for the final architectures, with the 98 billion parameter model incorporating 216 Transformer blocks (Appendix A.1.5).

This sentence is discussing the design of a computer program or system. The "final architectures" refer to the final design of the program or system. The "98 billion parameter model" is a specific version of the program or system that has a certain number of parameters. The "Transformer blocks" are a type of component used in the program or system. The "Appendix A.1.5" is a reference to a specific section of a document that provides more information about the design choices made. Overall, the sentence is saying that the final design of the program or system was influenced by the use of a certain type of component and that more information about the design can be found in a specific section of a document.###

Scaling ESM3 from 1.4 billion to 98 billion parameters results in substantial improvements in the validation loss for all tracks, with the greatest improvements observed in sequence loss (Fig.1D, Fig.S11).

The sentence is discussing the results of an experiment where a computer program called ESM3 was scaled up from 1.4 billion parameters to 98 billion parameters. The experiment found that this scaling led to significant improvements in the program's performance, particularly in reducing the amount of error or "loss" in the program's output. The improvements were observed across all tracks, but were most pronounced in the "sequence loss" track. The sentence also refers to a figure (Fig.1D) and a supplementary figure (Fig.S11) that provide visual representations of the results.

These gains in validation loss lead to better representation learning (Table S7 and Fig.S8).

This sentence is discussing the results of a study or experiment. The researchers found that when they improved the validation loss (a measure of how well a model is performing), they also saw improvements in representation learning (the ability of a model to accurately represent and understand data). The details of these improvements can be found in Table S7 and Figure S8.

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In single sequence structure prediction (Table S8) on CAMEO, ESM3 98B obtains 0.895 mean local distance difference test (LDDT) and surpasses ESMFold (0.865 LDDT).

This sentence is discussing the performance of a computer program called ESM3 98B in predicting the structure of a single sequence of amino acids. The program was tested on a dataset called CAMEO and achieved a score of 0.895 on a test called the mean local distance difference test (LDDT). This score indicates that the program was able to accurately predict the structure of the amino acid sequence. The sentence also mentions that ESM3 98B performed better than another program called ESMFold, which achieved a score of 0.865 on the same test.

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Unconditional generation produces high-quality proteins-with a mean predicted LDDT (pLDDT) 0.84 and predicted template modeling score (pTM) 0.52-that are diverse in both sequence (mean pairwise sequence identity 0.155 ) and structure (mean pairwise TM score 0.48 ), spanning the distribution of known proteins (Fig.1E, Fig.S13).

This sentence is describing the quality and diversity of proteins that were generated without any specific conditions. The proteins have a high predicted quality score and are diverse in both their sequence and structure. They also cover a wide range of known proteins. The sentence is using technical terms such as LDDT, pLDDT, pTM, and TM score to describe the quality and diversity of the proteins. User: