All Competitors

Multimodal molecular generation model for drug design, conditioned on properties, pharmacophores, protein sequences, or protein binding pockets.

Motif-aware graph diffusion model for controllable molecular generation that adapts to unseen properties by learning a lightweight task embedding.

Multimodal model that designs small molecules from transcriptomic and cell-imaging perturbation phenotypes with a rectified flow transformer.

Discrete graph diffusion model for multi-property molecular generation, composing per-property score guidance over arbitrary condition subsets.

Conditional chemical language model prompted with a protein target and mechanism of action to score and design molecules without structural input.

Generative chemistry foundation model pairing a text-and-SMILES language backbone with a 3D molecular point-cloud encoder for prediction and design.

Autoregressive 3D structure model built on an octree tokenizer, spanning molecule generation, molecular docking, and protein pocket prediction.

Secondary metabolite structure prediction from microbial biosynthetic gene clusters, generating SMILES strings from Pfam functional-domain tokens.

3D molecule generation that writes a valid 1D SELFIES string with a pretrained language model, then predicts its conformer with a diffusion module.

Autoregressive graph generator that flattens molecules into token sequences, letting a decoder-only transformer sample valid structures in one pass.

Discrete flow-matching generator for molecular and cell graphs, with sampling decoupled from training so a checkpoint is retuned, not retrained.