All Competitors

Transformer predicting microbial gene expression from an annotated genome alone, using protein language model embeddings of every coding sequence.

Protein-text foundation model aligning sequences with function descriptions through segment-wise objectives for static and dynamic functional sites.

Multi-omics instruction-tuned LLM that reads DNA, RNA, protein, and multi-molecule sequences and answers natural-language questions about them.

Phosphorylation site prediction from protein sequence. A LoRA-adapted ESM-2 encoder feeds a conformer, reaching 79.5% AUC at serine sites.

Structure-to-sequence protein design network inverting trRosetta, predicting coevolution features from a backbone to generate stable sequences.

Surrogate machine learning force field reusing a reference model's node features from earlier steps, running molecular dynamics eight times faster.

Structure-based molecular generation guided by imputed ligand electron density, assembling drug-like compounds into a pocket fragment by fragment.

Protein language model that generates paired heavy and light chain human antibodies from an antigen prompt, with binders validated in vitro.

Protein and RNA sequence annotation that also names the residues driving each label, learned from sequence-level supervision alone.

Retention time prediction for peptides whose post-translational modifications were never seen during training, using molecular-structure encodings.

Protein-ligand binding affinity prediction from multimodal representations. Retains accuracy on predicted rather than crystal complex structures.

Contact map and interface residue prediction for intrinsically disordered regions from sequence, outperforming AlphaFold-Multimer and AlphaFold3.

Geometric foundation model matching enzymes to the reactions they catalyze, trained on 1.5 million structure-informed enzyme-reaction pairs.

DNA- and RNA-binding residue prediction from a nucleic-acid-adapted protein language model and an equivariant graph network over protein structure.

Fragment-ion intensity prediction for cross-linked peptides, covering cleavable DSSO and DSBU chemistries alongside non-cleavable DSS and BS3.

Antibody CDR sequence and structure co-design from the whole antigen, using a relation-aware equivariant graph network with no specified epitope.

Cryo-EM heterogeneous reconstruction that models particles as one of K neural fields, resolving compositional and conformational states ab initio.

PROTAC degrader generation pipeline that screens target-binding fragments, then builds molecules under structure and physicochemical constraints.

Graph transformer over 3D protein structures predicting solvation free energy, hydrodynamic radius, diffusion constants, and molecular volume.

Multimodal foundation model integrating protein sequence, structure, and natural language to model and generate protein phenotypes across scales.

Antibody language model trained on human clonal families, proposing mutations that mimic in vivo affinity maturation for binding and stability.

HLA class II presentation and CD4 epitope prediction from peptide sequence, built on a protein language model with learned allele deconvolution.

Codon optimization framework pairing a frozen ProtBert encoder with a masked codon head, so every designed coding sequence translates back exactly.