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Model Overview

MegaMolBART is a model that understands chemistry and can be used for a variety of cheminformatics applications in drug discovery. The embeddings from its encoder can be used as features for predictive models. Alternatively, the encoder and decoder can be used together to generate novel molecules by sampling the model's embedding space.

Model Architecture

The model is a seq2seq transformer called a Bidirectional and Auto-Regressive Transformer (BART) [1]. It is based on a previous model, called Chemformer [2]. Pre-norm layer normalization and GELU activation are used throughout. This version of MegaMolBART has 8 layers, 4 attention heads, and a hidden space dimension of 256. Dropout was set to 0.1 during training.

Training

MegaMolBART was developed using the Nemo-Megatron framework. It was trained with data parallelism on 64 V100 GPUs (4 nodes x 16 GPUs) for 8 epochs (approximately 160k iterations or ~80 wall-clock hours) using a batch size of 32 molecules per GPU (micro batch). The Noam scheduler was used, with a peak learning rate value of 0.0005 and ~8000 warmup steps. FusedAdam optimization was used with parameters beta1 = 0.9 and beta2 = 0.999. Categorical cross-entropy loss was used to train the model.

Dataset

The ZINC-15 database was used for training [3]. Approximately 1.45 Billion molecules (SMILES strings) were selected from tranches meeting the following constraints: molecular weight <= 500 Daltons, LogP <= 5, reactivity level was \"reactive\", and purchasability was \"annotated\". The compounds were filtered to ensure a maximum length of 512 characters. Train, validation, and test splits were randomly split as 99% / 0.5% / 0.5%.

Data augmentation during training was performed via masking and SMILES randomization as described previously [2].

How to Use this Model

MegaMolBART can be run on hardware with access to any NVIDIA GPU with memory greater than 8 GB. The model can also be used for:

Fetching embeddings from the latent space representing a SMILES string
SMILES strings sampled around the region of a input SMILES molecule

References

Lewis M., Liu Y., Goyal N., Ghazvininejad M., Mohamed A., Levy O., Stoyanov V., and Zettlemoyer L., \"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension\", ArXiv, 2019, doi.
Irwin R., Dimitriadis S., He J., and Bjerrum E., \"Chemformer: A Pre-Trained Transformer for Computational Chemistry\", Mach. Learn.: Sci. Technol., 2022, doi.
Sterling T., and Irwin, J., Chem. Inf. Model, 2015, doi.

License

Apache License 2.0

A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code. The full copy of the license is available in the repository.

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The embeddings from its encoder can be used as features for predictive models. Alternatively, the encoder and decoder can be used together to generate novel molecules by sampling the model's embedding space.\n\n### Model Architecture\n\nThe model is a seq2seq transformer called a Bidirectional and Auto-Regressive Transformer (BART) [1]. It is based on a previous model, called Chemformer [2]. Pre-norm layer normalization and GELU activation are used throughout. This version of MegaMolBART has 8 layers, 4 attention heads, and a hidden space dimension of 256. Dropout was set to 0.1 during training.\n\n### Training\n\nMegaMolBART was developed using the Nemo-Megatron framework. It was trained with data parallelism on 64 V100 GPUs (4 nodes x 16 GPUs) for 8 epochs (approximately 160k iterations or ~80 wall-clock hours) using a batch size of 32 molecules per GPU (micro batch). The Noam scheduler was used, with a peak learning rate value of 0.0005 and ~8000 warmup steps. FusedAdam optimization was used with parameters beta1 = 0.9 and beta2 = 0.999. Categorical cross-entropy loss was used to train the model.\n\n### Dataset\n\nThe ZINC-15 database was used for training [3]. Approximately 1.45 Billion molecules (SMILES strings) were selected from [tranches](https://zinc15.docking.org/tranches/home/) meeting the following constraints: molecular weight <= 500 Daltons, LogP <= 5, reactivity level was \"reactive\", and purchasability was \"annotated\". The compounds were filtered to ensure a maximum length of 512 characters. Train, validation, and test splits were randomly split as 99% / 0.5% / 0.5%.\n\nData augmentation during training was performed via masking and SMILES randomization as described previously [2].\n\n### How to Use this Model\n\nMegaMolBART can be run on hardware with access to any NVIDIA GPU with memory greater than 8 GB. The model can also be used for:\n\n- Fetching embeddings from the latent space representing a SMILES string\n- SMILES strings sampled around the region of a input SMILES molecule\n\n### References\n\n1. Lewis M., Liu Y., Goyal N., Ghazvininejad M., Mohamed A., Levy O., Stoyanov V., and Zettlemoyer L., \"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension\", *ArXiv*, 2019, [doi](https://arxiv.org/abs/1910.13461).\n2. Irwin R., Dimitriadis S., He J., and Bjerrum E., \"Chemformer: A Pre-Trained Transformer for Computational Chemistry\", *Mach. Learn.: Sci. Technol.*, 2022, [doi](https://iopscience.iop.org/article/10.1088/2632-2153/ac3ffb).\n3. Sterling T., and Irwin, J., *Chem. Inf. Model*, 2015, [doi](http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00559).\n\n## License\n\n### Apache License 2.0\n\nA permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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