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| Model | Slug | Tags | Tokenizer | Predictor | Classifier | de novo Generator |
Explainer | SemanticSimilarity | Creation Pipeline |
|---|---|---|---|---|---|---|---|---|---|
| AbLang Heavy Chain API | ablang-heavy | antibody generator embeddings predict | ✗ | ✗ | - | ||||
| AbLang empowers researchers with capabilities like embeddings, sequence restoration, and likelihood computation, directly catering to the unique structural and functional attributes of heavy/light-chain antibodies. AbLang's deep learning framework, trained on an extensive collection of heavy-chain antibody sequences, offers unparalleled insights into their design and optimization for therapeutic applications. | |||||||||
| AbLang Light Chain API | ablang-light | antibody generator embeddings predict generate | ✗ | ✗ | - | ||||
| AbLang is an advanced AI language model tailored for antibody design, enabling embeddings, sequence restoration, and computing likelihoods for heavy/light-chain antibodies. Trained on a comprehensive database of human antibody sequences, AbLang can provide insights into antibody sequence functionality, and aid in the development of therapeutic antibodies. | |||||||||
| BioLMTox API | biolmtox | protein similarity toxin tokenize | ✗ | - | |||||
| We trained a NLP toxin detector to 98%+ recall, precision, and accuracy, creating a model that can recognize novel pathogenic sequences with as little as 60% identity to known toxins. Use this endpoint to screen pathogenic bacteria, potential neurotoxins and ion-channels, and other proteins. Obtain meaningful tokens related to pathogenicity. Perform semantic similarity searches. | |||||||||
| DNABERT-2 API | dnabert2 | dna BERT | - | ||||||
| Analyze DNA sequences with precision using, leveraging a pretrained BERT model for accurate genomic feature prediction and interpretation directly from raw DNA strings. | |||||||||
| DNABERT API | dnabert | dna BERT | - | ||||||
| This pre-trained language model surpasses conventional methods, providing superior accuracy and insights for DNA sequence classification, variant calling, and more. Ideal for researchers aiming to unlock genomic complexities efficiently. | |||||||||
| ESM-1v #1 API/v1 | esm1v_t33_650M_UR90S_1 esm1v_t33_650M_UR90S_2 esm1v_t33_650M_UR90S_3 esm1v_t33_650M_UR90S_4 esm1v_t33_650M_UR90S_5 |
enzyme protein maturation esm | ✗ | - | |||||
| First of five models in the ESM-1v series, each trained with a different seed. Predict favorable or unfavorable point-variants. NLP model trained on UniRef90. Zero-shot (unsupervised) predictor of functional effects. Ensemble with the remaining four models for best results. doi | |||||||||
| ESM1v ALL API | esm1v-all | enzyme protein maturation esm | ✗ | - | |||||
| Conveniently retrieve predictions from all five ESM-1v models for a given position, allowing for rapid variant ranking and deep mutational scans. | |||||||||
| ESM1v N1 API | esm1v-n1 | enzyme protein maturation esm | ✗ | - | |||||
| The 1st of the ESM-1v models, for unmasking AA positional likelihoods. | |||||||||
| ESM1v N2 API | esm1v-n2 | enzyme protein maturation esm | ✗ | - | |||||
| 2nd of the ESM-1v models. | |||||||||
| ESM1v N3 API | esm1v-n3 | enzyme protein maturation esm | ✗ | - | |||||
| 3rd ESM-1v model. | |||||||||
| ESM1v N4 API | esm1v-n4 | enzyme protein maturation esm | ✗ | - | |||||
| 4th ESM-1v model. | |||||||||
| ESM1v N5 API | esm1v-n5 | enzyme protein maturation esm | ✗ | - | |||||
| The 5th of the ESM-1v models, for unmasking AA positional likelihoods. | |||||||||
| ESM2 150M API | esm2-150m | enzyme protein embeddings esm logits | ✗ | - | |||||
| This 150M parameter ESM-2 model strikes a balance between accuracy and speed for embeddings, logits, attention and contact maps. | |||||||||
| ESM2 35M API | esm2-35m | enzyme protein embeddings esm logits | ✗ | - | |||||
| This ESM-2 model contains 35M parameter, and is one of the smaller models in the suite. | |||||||||
| ESM2 3B API | esm2-3b | enzyme protein embeddings esm logits | ✗ | - | |||||
| Use this 2nd-largest ESM-2 model for highly informative embeddings, contact maps, and more. | |||||||||
| ESM2 650M API | esm2-650m | enzyme protein embeddings esm | ✗ | - | |||||
| For most applications requiring slightly more accuracy, use this 650M parameter ESM-2 model for embeddings, contact maps, and more. | |||||||||
| ESM2 8M API | esm2-8m | enzyme protein embeddings esm logits | ✗ | - | |||||
| A small ESM-2 model, good for rapid development and applications requiring smaller datasets and quick response times. | |||||||||
| ESMFold Multi-Chain API | esmfold-multichain | protein structure prediction esm | ✗ | - | |||||
| For multi-chain proteins like antibodies, predict the folded structure in seconds with v2 of ESMFold. Similar results, speed, and accuracy to single-chain folding endpoint, but now available for more complex sequences. PDBs via REST in less than a minute, using one of the largest protein LMs to date. doi | |||||||||
| ESMFold Single-Chain API | esmfold-singlechain | protein structure prediction esm | ✗ | - | |||||
| Predict structure using Meta's SOTA language model. Similar in accuracy to AlphaFold2.0 and RoseTTAFold; magnitudes faster. Get predicted PDBs via REST in seconds, with one of the largest protein LMs to date. doi | |||||||||
| ESM Inverse Fold API | esm-if1 | protein generator esm | ✗ | - | |||||
| ESM-IF1 Inverse Folding enables the generation of novel protein sequences tailored to fold into a specified 3D structure, as denoted by a PDB string. | |||||||||
| ProGen2 BFD90 API | progen2-bfd90 | protein generator gpt | ✗ | - | |||||
| BFD-90 pretrained model from the suite of ProGen2 generative models. Tune outputs with your choice of pretrained model, temperature, length, and more. | |||||||||
| ProGen2 Large API | progen2-large | protein generator gpt | ✗ | - | |||||
| ProGen2 Large contains 2.7B parameters and is the second-largest model in the suite of generative models. Tune outputs with your choice of pretrained model, temperature, length, and more. | |||||||||
| ProGen2 Medium API | progen2-medium | protein generator gp2 | ✗ | - | |||||
| For faster protein generation, use this 764M parameter model from the ProGen2 suite of generative models. Tune outputs with your choice of pretrained model, temperature, length, and more. | |||||||||
| ProGen2 OAS API | progen2-oas | antibody generator gpt | ✗ | - | |||||
| ProGen2 OAS was trained on the Observed Antibody Space, making it more suitable to some generative-antibody applications. Tune outputs with your choice of pretrained model, temperature, length, and more. | |||||||||
| ProstT5 AA2Fold API | prostt5-aa2fold | folding proteins | ✗ | - | |||||
| ProstT5 Fold2AA API | prostt5-fold2aa | folding proteins | ✗ | - | |||||
| ProteInfer EC (Enzyme Commmision) API | proteinfer-ec | enzyme protein prediction EC | ✗ | - | |||||
| ProteInfer EC Prediction API leverages AI to accurately predict enzyme commission (EC) numbers from protein sequences, enhancing enzymatic research and biotechnological applications. | |||||||||
| ProteInfer GO (Gene Ontology) API | proteinfer-go | protein GO predictor function | ✗ | - | |||||
| Harness the power of deep learning to predict Gene Ontology (GO) terms for proteins directly from sequence data. ProteInfer leverages a comprehensive dataset and advanced models to provide accurate functional annotations, offering insights into biological processes, cellular components, and molecular functions. | |||||||||
| UniRef50 Embedding Similars API | uniref50-similars | ✗ | - | ||||||
| Accelerate your protein sequence searches with our blazing-fast Nearest Neighbors Search API. By leveraging protein language models to generate vector embeddings for over 65 million UniRef50 sequences, our service enables lightning-fast similarity searches that are 1,200 times faster than traditional Levenshtein-based methods like BLAST. | |||||||||
| ZymCTRL API | zymctrl | enzymes generation | ✗ | - | |||||
Create Your Own With
| Language Model | Slug | Classifier | Regressor | Generator | Tags |
|---|---|---|---|---|---|
| Protein Classifier | finetune_esm2_classifier | ✗ | protein classifier | ||
| Start with one of the largest protein language models, trained on hundreds of millions of proteins. Then finetune it on your own sequences, to make it specific to your classification task. Creates a GPU-backed API endpoint for classifying new sequences, retrieving probabilities, with your personalized ESM2 model. doi | |||||
| Protein Generator | finetune_protgpt2_generator | ✗ | protein generator | ||
| Finetune a generative protein model on your own data, then create new sequences via API. Create de-novo best representative sequences that "follow the principles of natural ones and sample unexplored protein space". Only positive-class sequences required. doi | |||||
| DNA Classifier | finetune_dnabert_classifier | ✗ | dna BERT | ||
| DNA-based BERT models have demonstrated performance on promoter prediction, splice sites and transcription factor binding sites, up to 99% AUC. Bring your own NGS data or relevant public datasets to create any classifier. Your model also comes with an ability to export meaningful DNA BERT embeddings, perform semantic similarity searches. doi | |||||