All Competitors

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.

Generative chemistry ensemble fine-tuned on 2.4M RNA-small molecule binding measurements, designing analogues around a seed compound.

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.

Foundation model for paired ECG Lead-II and PPG waveforms, fusing the two through cross-modal attention and adaptive residual vector quantization.

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

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

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.

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

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

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.

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

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

Enzyme function prediction that scores whether two sequences catalyze the same reaction, via attention pooling over frozen ESM Cambrian embeddings.

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

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

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

Causal multimodal transformer that embeds the do-operator in attention to predict single-cell gene expression under unseen genetic perturbations.

Protein language model aligning ESM sequence embeddings with molecular dynamics trajectories for zero-shot mutation effect and stability prediction.

Photoplethysmography foundation model pretrained by reconstructing masked ECG from PPG, learning cardiac timing structure for wearable health tasks.