Protein Language Models
ESMFold2
Predicts protein, DNA, RNA, and ligand complexes
Candido et al. 2026
ESMFold2 Binder Design
Design de novo protein binders against a target
Candido et al. 2026
ESM-C 6B
ESM-C 6B protein language model
Candido et al. 2026
PepMLM
Design linear peptide binders
Chen et al.
Profluent E1
Protein language model
Jain et al.
Antibody Evolution
Language models to recommend mutations for increased antibody binding affinity
Hie et al.
ZymCTRL
Conditionally generate artificial enzymes
Illanes-Vicioso et al.
EvoNB
Mutate nanobody sequences
Xiong et al.
AbGPT
Generate antibody sequences
Kuan et al.
HELM-GPT
Generate macrocyclic peptides
Xu et al.
CatPred
ESM-2 based kcat, Km, and Ki prediction
Boorla et al.
SaProt
Language models for protein property prediction
Su et al.
SynCodonLM
Codon language model
Heuschkel et al.
NetSolP
Solubility and usability based on protein language models
Thumuluri V et al.
TEMPRO
Nanobody melting temperature prediction
Alvarez et al.
SaProtΔG
Absolute protein stability (ΔG) prediction from structure
Cho et al.
ESM-IF ΔG
Absolute protein folding stability (ΔG) prediction from structure with ESM-IF
Cagiada et al.
PALM
Protein aggregation propensity prediction
Eschbach et al.
CANYA
Amyloid nucleation propensity prediction
Thompson et al.
PolyXpert
Antibody polyreactivity prediction
DELPHI
Antibody polyreactivity and monomer-purity prediction
Nguyen et al.
TemStaPro
Protein thermostability prediction
Pudžiuvelytė et al.
EvoProtGrad
Generate mutations with directed evolution
Emami et al.
Structural Evolution
Mutate protein complexes with structure-informed language model
Varun R. Shanker et al. et al.
ESMFold
Predict protein structure given a sequence
Lin et al.
SaProt Finetuning
SaProt finetuning
Su et al.
Asparagine Deamidation Prediction
Asparagine deamidation prediction, finetuned on your data
Ahmed et al.
Finetune Protein Language Model
Protein sequence property prediction finetuning
Schmirler et al.
PLM Crystallization Prediction
Sequence-based crystallization predictor. Mean-ensemble of LightGBM classifiers trained on ESM2 T30-150M and ESM2 T36-3B embeddings.
Mall et al.
BioReason-Pro
Predict protein function with a written reasoning trace
Fallahpour A et al.
DeepFRI
Predict protein function
Gligorijevic et al.
AbLang2
Antibody language model
Olsen et al.
AbMAP
Antibody language model
Singh et al.
AntiBERTy
Optimize antibody/nanobody sequence affinity
Ruffolo et al.
Seq2Symm
Predict protein homo-oligomer symmetry
Kshirsagar et al.
deepSTABp
Predict protein melting temperature
Jung et al.
ESM2 Embeddings
Generate ESM2 Embeddings
Lin et al.
Prot T5 XL Embeddings
Generate Prot T5 Embeddings
Elnaggar et al.
ESM2
Score point mutations or mask residues using ESM-2 language model
Lin et al.
AMPLIFY
Score using AMPLIFY language model
Fournier et al.
BALM Paired
Score point mutations using BALM-paired language model
Burbach et al.
ESM3
Generative protein language model. Fill in masked positions of a sequence, optionally conditioned on a structure, or predict structure from sequence.
Hayes et al.
ESM-Scan
Score point mutations using ESM1b language model
Lin et al.
ProGen2 Finetuning
Finetune generative protein language model
H. Hrbáň and D. Hoksza et al.
ProtRL Finetuning
Reinforcement-learning finetuning of a protein language model
Stocco et al.
ProtRL Steering
Steer a protein language model toward a family or a score as it writes new sequences
Garibbo et al.
SeqDesign Model Training
Train a SeqDesign model on your own protein family
Shin et al.
MAGE
De novo antibody generation from antigen sequence using language models
Wasdin P.T. et al.
LICHEN
Generate light chain sequences conditioned on heavy chain using machine learning
Capel H.L. et al.
DynamicsPLM Finetuning
Finetune a dynamics-aware protein language model on your data
Kalifa et al.
Dyna-1
Predict millisecond conformational exchange
Wayment-Steele et al.
ESMC Finetune
Finetune ESM-C on labelled sequence data (regression, classification, token-classification, or MLM)
Candido et al. 2026
ProFam
Protein family language model
Wells et al.