All Competitors

Target-conditioned generative language model that designs drug-like SMILES from a protein sequence, tuned on regulator-approved drug-target pairs.

Structure-based molecule optimization that steers a Bayesian flow network with property gradients over 3D coordinates and atom types at once.

Chemical language model of the human metabolome that generates and ranks candidate structures for unidentified mass spectrometry peaks.

De novo enzyme design conditioned on the reaction to be catalysed: substrate and product SMILES in, catalytic pocket, enzyme, and docked complex out.

Pocket-conditioned 3D ligand generator trained on its own predicted conformations, closing the train-inference gap that degrades diffusion sampling.

Cell Painting image generation conditioned on a control well image and a compound's structure, covering cell lines and chemicals never trained on.

Chemical language model generating SMILES on a recurrent xLSTM backbone, designing within an unseen molecular domain from a few in-context examples.

Molecular property prediction from 3D structure for assays with dozens of labels, pretrained by extreme denoising plus DFT and LLM auxiliary labels.

Chemical language model for molecular property prediction, pretrained on PubChem SMILES by masking whole functional groups rather than random tokens.

Molecular scaffold optimization that grafts generated fragments onto a lead compound, guided by Bayesian search in a conditional VAE latent space.

Protein-ligand binding affinity prediction that fine-tunes ESM-2 and ChemBERTa-2 into a shared space where cosine similarity is the predicted pKd.

SMILES transformer pretrained to predict 113 RDKit molecular descriptors, giving embeddings that carry physicochemical properties into ADMET models.

Reprograms a frozen single-target diffusion model for dual-target drug design by composing SE(3)-equivariant messages across two aligned pockets.

Peptide representation model that DoRA-tunes ChemBERTa on 100,000 modified and bioactive peptide SMILES for therapeutic property prediction.

Multi-modal, multi-task biological foundation model trained on 2 billion samples spanning proteins, small molecules, and single-cell gene expression.

Text-guided molecule generation by linking a pretrained scientific text encoder to a frozen molecular language model with a cross-attention adapter.

Chemical perturbation model generating post-treatment transcriptomes for compounds and cell lines never screened, from SMILES structure and dose.

Molecular foundation model that late-fuses graph, image, and SMILES encoders into one embedding for molecular property and drug target prediction.

All-atom structure tokenizer that turns proteins, RNA and small molecules into discrete 3D tokens and decodes them back below 1 Å RMSE.

Diffusion model that jointly generates a 3D molecule and its shape, electrostatic surface and pharmacophores for ligand-based drug design.

Molecular docking and design foundation model that unifies structure-based drug design and peptide design at the atom level in one checkpoint.

Light-weight rigid protein-ligand docking model adapting the AlphaFold2 architecture, with a binding affinity module for virtual screening.

Blind protein-ligand docking as a single transformer pass over distance matrices, at hundredths of a second per complex on one GPU.