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Frontier protein language models (300M/600M/6B) trained on billions of protein sequences, establishing a new unsupervised scaling frontier beyond ESM2 with emergent long-range structural understanding; ships with ESMFold2 structure prediction (SOTA DockQ pass-rates on Foldbench protein-protein and antibody-antigen complexes, lab-validated de novo binder/scFv design protocol) and the ESM Atlas mapping 6.8B proteins with sparse-autoencoder-interpretable world-model features (2.9K+ stars, 2025-2026)

Flow-based generative model for atomistic protein binder design with test-time optimization, SOTA on binder benchmarks (ICLR 2026 Oral, NVIDIA)

Accessible protein design platform via Google Colab integrating AlphaFold2, RoseTTAFold, and ProteinMPNN for de novo hallucination, fixed backbone design, and binder design (Sergey Ovchinnikov, 2022+)

98B-parameter frontier generative model jointly reasoning over protein sequence, structure, and function, trained on 2.78 billion proteins; generated a novel fluorescent protein (esmGFP) with only 58% sequence identity to known GFPs (EvolutionaryScale, 2024)

Latent-space probabilistic denoising diffusion model for predicting coarse-grained conformational ensembles of intrinsically disordered proteins and regions from sequence, with GPU/CPU inference, trajectory export, and FAISS-based similarity search (67+ stars, LGPL-3.0)

All-atom generative world model for all-to-all biomolecular interaction design, enabling cross-modality generation of proteins, nucleic acids, small molecules, and cyclic peptides with fine-grained epitope-level control and 2-4 orders of magnitude faster design throughput than modality-specific baselines (316+ stars, Apache 2.0)

All-atom biomolecular structure prediction for protein-nucleic acid-small molecule-metal ion complexes, enabling accurate modeling of covalent modifications and assemblies beyond proteins (Baker Lab, Science 2024)

Baidu's open-source reproduction of AlphaFold3 in PaddlePaddle, providing pretrained weights and inference pipelines for unified biomolecular structure prediction across proteins, nucleic acids, ligands, ions, and post-translational modifications within the PaddleHelix biocomputing platform (Baidu, bioRxiv 2024)

Multimodal deep learning framework integrating peptide-MHC protein sequence, structure, and biochemical properties to predict class-I immunogenicity for infectious disease epitopes and cancer neoepitopes with cancer-wildtype contrastive learning, enabling personalized vaccine design (Krishnaswamy Lab, Yale University)

Graph neural network operating entirely at the atomic level for protein-ligand conformational ensemble prediction and docking, generating diverse solutions through rapid stochastic denoising to model conformational heterogeneity (Baker Lab, bioRxiv 2025)

AI-assisted mutation nomination approach optimizing protein function by integrating structural and evolutionary constraints into protein inverse folding models, compatible with ProteinMPNN, LigandMPNN, ESM-IF1, and SaProt (Chinese Academy of Sciences, 359+ stars)

Large-scale flow-based protein backbone generator utilizing hierarchical fold class labels for conditioning with a tailored scalable transformer architecture, enabling controllable de novo protein design (264+ stars)

In silico directed evolution framework using few-shot active learning to optimize protein activities, enabling rapid protein engineering with minimal experimental data (352+ stars, 2023)