All Competitors

Sequence-based discrete-diffusion framework that designs peptide binders with specified agonist or antagonist behavior against GPCR targets.

Multimodal Q-former that fuses DNA sequence, gene context, protein function, and text for zero-shot variant interpretation with a frozen LLM.

Sparse autoencoders trained on protein language model embeddings to expose interpretable features and drive zero-shot variant effect prediction.

370M-parameter ligand-conditioned discrete diffusion model that co-designs protein sequence and structure under explicit small-molecule constraints.

Domain-specific foundation model for zero-shot plant root image segmentation, built on a MobileSAM backbone and trained across nine root datasets.

Spatial transcriptomics deconvolution foundation model whose rank-based spot encoding transfers across tissues and platforms without retraining.

Protein conformational ensemble tokenizer that learns a discrete alphabet of states from molecular dynamics, reusable as a frozen feature layer.

Multiscale graph neural network for fixed-backbone protein binder sequence design with a contrastive decoding algorithm to improve target selectivity.

Conditional discrete diffusion model for protein variant generation, with a calibrated identity dial controlling drift from a wild-type sequence.

Unified bio-language Mixture-of-Experts model spanning DNA, protein sequence and structure, and biological text across eight task families.

Signed heterogeneous graph foundation model over the SIGMA-KG knowledge graph, predicting drug mode of action and drug-drug interactions zero-shot.

Transformer tractography model for mouse-brain diffusion MRI, guided by axonal priors learned from Allen Mouse Brain Connectivity Atlas streamlines.

Bayesian generative model that embeds gene regulatory networks into RNA velocity inference, enabling cell fate mapping and in silico perturbation.

Latent diffusion model for PTM-aware protein sequence design, using ControlNet-style conditioning to steer generation toward chosen PTM sites.

Microbiome world model that treats a community as a set of taxa, scoring how well each member fits and predicting community dynamics zero-shot.

Multi-organ foundation model aligning histology images with spatial-transcriptomics profiles for zero-shot expression and survival prediction.

RNA inverse-folding language model that designs nucleotide sequences satisfying a target secondary structure, fixed bases, and coding constraints.

Virtual cell foundation model pretrained on over 23 million cells from 5,000 patient samples for drug target and biomarker discovery.

Discrete diffusion model for conditional antibody sequence design with germline-absorbing noising that focuses learning on somatic variation.

Protein sequence embedding model, contrastively fine-tuned from ESM-2, that places functionally and structurally related proteins close together.

DNA language model combining Mamba state-space layers, gated dilated convolutions, and Fourier attention to capture multi-scale regulatory patterns.

Microbiome foundation models that treat microbial community composition as a language, enabling zero- and few-shot transfer across prediction tasks.

All-atom protein co-design model that generates sequence and structure together in one unified diffusion process, aimed at hard binder design.

Three fixed ProtGPT2 fine-tunes specialized for metalloprotein generation, trained on ProteinMPNN-derived synthetic sequences.