Update README.md with appropriate references

README.md

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-license: cc-by-4.0
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-
-# Protein Sequence Modelling with Bayesian Flow Networks
-
-Welcome to the model weights for the paper "Protein Sequence Modelling with Bayesian Flow Networks". Using the [code on our GitHub page](https://github.com/instadeepai/protein-sequence-bfn), you can sample from our trained models ProtBFN, for general proteins, and AbBFN, for antibody VH chains. 
-
-[Bayesian Flow Networks](https://arxiv.org/abs/2308.07037) are a new approach to generative modelling, and can be viewed as an extension of diffusion models to the parameter space of probability distributions. They define a continuous-time process that maps between a naive prior distribution and a psuedo-deterministic posterior distribution for each variable independently. By training our neural network to 'denoise' the current posterior, by taking into account mutual information between variables, we implicitly minimise a variational lower bound. We can then use our trained neural network to generate samples from the learned distribution.
-
-One of the benefits of defining such a process in probability parameter space is that it can be applied to *any* family of distributions with continous-valued parameters. This means that BFNs can be directly applied to discrete data, allowing for diffusion-like generative modelling for sequences without restrictive left-to-right inductive biases or relying on discrete-time stochastic processes. The main focus of our work is to investigate the application of BFNs to *protein sequences*, as represented by a sequence of amino acids. The ProtBFN methodology is broadly summarised below:
-
-![An overview of ProtBFN.](BFN_overview.png)
-
-Having trained ProtBFN, we find that it is exceptionally performant at unconditional generation of de novo protein sequences. For example, we find that we are able to rediscover a variety of structural motifs, according to structures predicted by ESMFold, with high sequence novelty:
-
-![Cath hits for ProtBFN.](cath_s40_proteins.png)
+---
+license: cc-by-4.0
+---
+
+# Protein Sequence Modelling with Bayesian Flow Networks
+
+  Welcome to the model weights for the paper ["Protein Sequence Modelling with Bayesian Flow Networks"](https://www.biorxiv.org/content/10.1101/2024.09.24.614734v1). Using the [code on our GitHub page](https://github.com/instadeepai/protein-sequence-bfn), you can sample from our trained models ProtBFN, for general proteins, and AbBFN, for antibody VH chains. 
+
+[Bayesian Flow Networks](https://arxiv.org/abs/2308.07037) are a new approach to generative modelling, and can be viewed as an extension of diffusion models to the parameter space of probability distributions. They define a continuous-time process that maps between a naive prior distribution and a psuedo-deterministic posterior distribution for each variable independently. By training our neural network to 'denoise' the current posterior, by taking into account mutual information between variables, we implicitly minimise a variational lower bound. We can then use our trained neural network to generate samples from the learned distribution.
+
+One of the benefits of defining such a process in probability parameter space is that it can be applied to *any* family of distributions with continous-valued parameters. This means that BFNs can be directly applied to discrete data, allowing for diffusion-like generative modelling for sequences without restrictive left-to-right inductive biases or relying on discrete-time stochastic processes. The main focus of our work is to investigate the application of BFNs to *protein sequences*, as represented by a sequence of amino acids. The ProtBFN methodology is broadly summarised below:
+
+![An overview of ProtBFN.](BFN_overview.png)
+
+Having trained ProtBFN, we find that it is exceptionally performant at unconditional generation of de novo protein sequences. For example, we find that we are able to rediscover a variety of structural motifs, according to structures predicted by ESMFold, with high sequence novelty:
+
+![Cath hits for ProtBFN.](cath_s40_proteins.png)
+
+
+## Cite our work
+
+If you have used ProtBFN or AbBFN in your work, you can cite us using the following bibtex entry:
+
+```text
+@article {Atkinson2024.09.24.614734,
+	author = {Atkinson, Timothy and Barrett, Thomas D. and Cameron, Scott and Guloglu, Bora and Greenig, Matthew and Robinson, Louis and Graves, Alex and Copoiu, Liviu and Laterre, Alexandre},
+	title = {Protein Sequence Modelling with Bayesian Flow Networks},
+	elocation-id = {2024.09.24.614734},
+	year = {2024},
+	doi = {10.1101/2024.09.24.614734},
+	publisher = {Cold Spring Harbor Laboratory},
+	URL = {https://www.biorxiv.org/content/early/2024/09/26/2024.09.24.614734},
+	eprint = {https://www.biorxiv.org/content/early/2024/09/26/2024.09.24.614734.full.pdf},
+	journal = {bioRxiv}
+}
+```