Protein Structure Prediction Patents

Protein structure prediction patents cover multiple sequence alignment MSA attention and transformer architecture innovations; invariant point attention IPA and equivariant SE(3) frame-based structure prediction innovations; protein language model and embedding representation innovations; and protein structure database curation and retrieval innovations — with IP held by AI research labs, pharma companies, and structure prediction software companies: MAJOR PROTEIN STRUCTURE PREDICTION PATENT HOLDERS: DEEPMIND/GOOGLE: 300+; specific AlphaFold innovations (specific specific AlphaFold2: specific specific Evoformer 48 blocks from specific specific MSA multiple sequence alignment row attention from specific specific MSA column attention global from specific specific outer product mean pair update from specific specific pair representation update from specific specific template IPA invariant point attention from specific specific backbone frame update from specific specific all-atom structure module from specific specific FAPE frame-aligned point error loss from specific specific confidence pLDDT per-residue 0-100 from specific specific backbone torsion angles φ ψ ω χ1-4 from specific specific AlphaFold Database AF-DB 200M+ structures 2022 from specific specific AlphaFold3: specific specific Pairformer replaces Evoformer from specific specific diffusion module SE(3) reverse from specific specific 170M parameter core network from specific specific protein DNA RNA ligand small molecule from specific specific CCD Chemical Component Dictionary from specific specific MSA subsampling 512 sequences from specific specific confidence pTM iPTM from specific specific CIF mmCIF coordinate output); BAKER LAB/UNIVERSITY OF WASHINGTON: 200+; META AI/FAIR: 100+; SCHRODINGER: 300+; RELAY THERAPEUTICS: 100+.

Multiple sequence alignment MSA encoding row column attention and outer product mean innovations; SE(3)-equivariant invariant point attention IPA and frame-based backbone representation innovations; and protein language model ESM LLM embedding and confidence pLDDT calibration innovations represent core protein structure prediction patent domains: MSA AND TRANSFORMER PATENTS: DEEPMIND; BAKER LAB; MICROSOFT; NVIDIA: specific MSA/transformer innovations (specific specific MSA transformer: specific specific AlphaFold2 Evoformer block from specific specific 48 Evoformer iterations from specific specific MSA row-wise gated attention dim 64 from specific specific MSA column-wise attention dim 64 from specific specific extra MSA stack from specific specific row/column split complexity O(N_seq × N_res) vs. O(N_seq × N_res²) from specific specific outer product mean: specific specific MSA_i × MSA_j outer product from specific specific mean over sequences from specific specific pair representation update from specific specific triangle update: specific specific triangle multiplicative update outgoing from specific specific triangle multiplicative update incoming from specific specific triangle attention starting/ending nodes from specific specific Rosettafold two-track network: specific specific 1D sequence track from specific specific 2D distance matrix track from specific specific 3D coordinates from specific specific SE-3 equivariant from specific specific 50M+ structure predictions); INVARIANT POINT ATTENTION PATENTS: DEEPMIND; ALPHAFOLD; BAKER LAB; PRESCIENT: specific IPA innovations (specific specific IPA invariant point attention: specific specific backbone frame T= (R, t) rotation+translation from specific specific local queries local keys local values from specific specific pair bias from specific specific point cloud 4 points per head from specific specific O(N²) attention from specific specific 8 attention heads from specific specific point distance penalty from specific specific backbone update: specific specific protein backbone N-Cα-C frame from specific specific φ ψ ω backbone torsion from specific specific χ1-4 side chain rotamer from specific specific FAPE loss: specific specific frame-aligned point error from specific specific all-atom all-frame loss from specific specific violation loss clash stereo from specific specific violation geometry bond lengths angles from specific specific 1.4 Å Cα RMSD typical from specific specific < 1 Å for ordered domains); PROTEIN LANGUAGE MODEL PATENTS: META AI; MICROSOFT; NVIDIA; EVOLUTIOINTERNATIONAL: specific language model innovations (specific specific ESM-2: specific specific 650M 3B 15B 48B parameters from specific specific 250M UniRef50/UniRef90 sequences from specific specific masked language model MLM 15% mask from specific specific contact prediction from specific specific pLDDT confidence embedding from specific specific ESMFold: specific specific single sequence folding no MSA from specific specific 15 min folding vs. AF2 hours from specific specific 617M structures PDB-quality from specific specific ESM3: specific specific multimodal sequence structure function from specific specific 700M tokens generative from specific specific ProtTrans ProtBERT: specific specific BERT bert large from specific specific T5 XL 3B protein from specific specific Rostlab BFD training from specific specific ProteinMPNN: specific specific Baker Lab graph neural net from specific specific inverse folding from specific specific given backbone predict sequence from specific specific 52% recovery rate from specific specific 10⁶ sequences/second CPU).

RFdiffusion and BindCraft de novo protein backbone and binder design innovations; structure-based drug design SBDD virtual screening and docking innovations; and free energy perturbation FEP computational binding affinity prediction innovations represent additional protein structure prediction adjacent patent domains: DE NOVO PROTEIN DESIGN PATENTS: BAKER LAB; PROTEIN AI; EVOZYNE; NABLA BIO: specific de novo design innovations (specific specific RFdiffusion: specific specific Baker Lab 2023 from specific specific diffusion over backbone frame from specific specific SE(3) equivariant denoising from specific specific conditional motif scaffold from specific specific 10⁶ novel protein backbones from specific specific BindCraft: specific specific binder design to specific target from specific specific RFdiffusion + ProteinMPNN + AF2 verification from specific specific 10 μM Kd binders without experimental from specific specific high-throughput library from specific specific hallucination: specific specific hallucination gradient descent from specific specific CLIP-like loss from specific specific soluble expression from specific specific protein DESIGN SPEC: specific specific TM-score target fold from specific specific 0.9 TM-score RFdiffusion from specific specific ProteinMPNN sequence: specific specific 52% recovery given backbone from specific specific MPNN message passing from specific specific AF2 verification: specific specific pLDDT >80 confident from specific specific pTM iPTM multimer from specific specific wet lab screening 10-100 candidates from specific specific GFP-fusion yeast display from specific specific FACS sorting from specific specific SPR Biacore 8K affinity); STRUCTURE-BASED DRUG DESIGN PATENTS: SCHRODINGER; OPENEY; MOLSOFT; CCDC: specific SBDD innovations (specific specific molecular docking: specific specific Glide XP GScore from specific specific docking grid receptor preparation from specific specific Glide SP standard precision from specific specific XP extra precision 1,000 pose limit from specific specific induced fit docking IFD from specific specific cross-docking validation from specific specific virtual screening: specific specific library 10M-1B compounds from specific specific HTVS high-throughput 1B from specific specific Enamine REAL space 48B from specific specific FEP free energy perturbation: specific specific RBFE relative binding free energy from specific specific ABFE absolute binding free energy from specific specific FEP+ Schrodinger 0.5-1.0 kcal/mol RMSD from specific specific GROMACS OpenMM Amber GPU FEP from specific specific 10-100 ns per perturbation from specific specific 10-20 kcal/mol range from specific specific ADME: specific specific pKa lipophilicity logD from specific specific QikProp ADME predictor from specific specific CNS MPO score from specific specific CYP450 metabolic stability from specific specific Caco-2 permeability from specific specific PAMPA parallel artificial membrane); AI DRUG DISCOVERY PATENTS: EXSCIENTIA; RECURSION; INSILICO MEDICINE; SCHRODINGER: specific AI drug discovery innovations (specific specific Exscientia DSAR: specific specific drug scaffold AI reinforcement from specific specific GPT molecule generation from specific specific Phase I clinical EXS-21546 from specific specific Insilico Medicine PandaOmics: specific specific GAN generative adversarial network from specific specific AlphaFold structure-based target from specific specific INS018-055 fibrosis Phase I 2023 first AI-designed drug from specific specific Recursion OS: specific specific perturbational imaging CellPainting from specific specific 12M Cell Painting images from specific specific feature extraction CNN from specific specific 3.2T dataset from specific specific NVIDIA BioNeMo: specific specific protein LLM service API from specific specific ESM-2 AlphaFold ProteinMPNN).

Protein structure prediction startup IP strategy must navigate DeepMind AlphaFold2 Evoformer MSA attention patents (300+), Baker Lab RoseTTAFold and RFdiffusion de novo design patents (200+), Meta AI ESMFold language model patents (100+), Schrodinger FEP+ and Glide docking patents (300+), and Exscientia/Insilico generative drug design patents (200+); understand that AlphaFold2 and AlphaFold3 model weights are open-sourced (CC BY 4.0 for non-commercial, commercial license required) but Evoformer architecture and training methodology are patentable; identify whitespace in novel protein language model for specific therapeutic modality (antibody, enzyme, peptide), novel multi-state conformational ensemble prediction, novel protein-protein interaction PPI inhibitor design, and novel generative model for covalent warhead drug design — while understanding that AlphaFold2 and ESMFold have transformed the protein structure prediction field and the AI drug discovery market is projected to exceed $4B by 2030: PROTEIN STRUCTURE PREDICTION STARTUP IP STRATEGY: UNDERSTAND THE PROTEIN STRUCTURE PREDICTION PATENT LANDSCAPE — DEEPMIND ALPHAFOLD2 AND BAKER LAB RFDIFFUSION HOLD BROAD MSA ATTENTION AND DE NOVO DESIGN IP: DeepMind Evoformer 48-block MSA row/column attention patents and Baker Lab RFdiffusion SE(3) equivariant diffusion backbone design patents cover the core prediction and design architectures — new entrants need novel architecture (hierarchical MSA, sparse attention, faster single-sequence method beyond ESMFold), novel training objective, or novel application domain; NOVEL MULTI-STATE CONFORMATIONAL PREDICTION AND NOVEL COVALENT DRUG DESIGN GENERATION ARE HIGHEST-VALUE LEAST-CONSOLIDATED IP: After AlphaFold3 (2024) protein-ligand complex prediction and RFdiffusion binder design, novel multi-state conformational ensemble (cryptic pocket prediction, intrinsically disordered protein IDP modeling), novel covalent warhead placement in binding pocket, and novel antibody CDR de novo generation represent less consolidated patent territory; STRUCTURE-BASED DRUG DESIGN FEP+ AND VIRTUAL SCREENING PIPELINE ARE PATENT-VIABLE: Novel FEP free energy protocol beyond Schrodinger FEP+ RMSD <1 kcal/mol, novel dual-topology lambda scheduling, novel ML surrogate FEP surrogate model, and novel AI-guided synthesis accessibility scoring represent patentable improvements to the SBDD pipeline; WHEN TO PATENT IN PROTEIN STRUCTURE PREDICTION: NOVEL MODEL WITH MEASURED STRUCTURE PREDICTION ACCURACY AND DRUG DISCOVERY UTILITY: specific novel protein structure model or drug design pipeline (specific specific architecture type + specific specific TM-score on CASP14/15 blind test set + specific specific GDT_TS global distance test + specific specific pLDDT calibration + specific specific folding time + specific specific binding affinity RMSD kcal/mol) vs. specific AlphaFold2 TM-score 0.92 CASP14 GDT_TS 92.4 pLDDT <2 Å median or specific FEP+ RBFE 0.5-1.0 kcal/mol RMSD baseline — measured TM-score, pLDDT calibration, and FEP RBFE RMSD kcal/mol vs. AlphaFold2 or Schrodinger FEP+ baseline is the critical protein structure prediction IP metric; KEY FTO CHECKLIST: DeepMind AlphaFold2 Evoformer 48 blocks MSA row/column outer product IPA backbone frame FAPE φψω CASP14 TM-score 0.92 GDT_TS 92.4 AF-DB 200M; AlphaFold3 Pairformer diffusion SE(3) protein DNA RNA ligand pTM iPTM CIF; Baker Lab RFdiffusion SE(3) denoising 10⁶ backbones BindCraft 10 μM; ProteinMPNN inverse folding 52% recovery; Meta AI ESM-2 650M-15B MLM ESMFold 617M 15min; Schrodinger FEP+ RBFE ABFE 0.5-1.0 kcal/mol Glide XP IFD 10M-1B VS HTVS; Exscientia DSAR GPT Phase I EXS-21546; Insilico INS018-055 fibrosis Phase I 2023; Recursion CellPainting 12M images NVIDIA BioNeMo ESM-2 API.