All Competitors

Structure-based drug design that generates molecules as synthetic pathways over 1.2M purchasable building blocks, so each design carries its route.

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

Multimodal LLM for inverse molecular design, interleaving text and graph generation with a diffusion transformer and A* retrosynthetic planning.

Pharmacophore-conditioned generator that emits molecules as synthetic trees of Enamine building blocks, so every design carries a synthesis route.

Chemical language model that tokenizes each atom by its functional group, giving transferable embeddings for molecular property prediction.

Enzyme catalytic pocket design conditioned on a reaction: substrate and product in, pocket backbone, sequence, and EC class out.

Equivariant graph neural networks pretrained by denoising, used frozen as geometric featurizers for analyzing conformational dynamics in simulations.

Chemical language model that grows molecules one fragment at a time from one or two input fragments, for hit-to-lead design and PROTAC linkers.

Protein-ligand docking network that builds all-atom conformational ensembles of small molecules and side chains from a fixed protein backbone.

Predicts which olfactory receptors an odorant activates, fusing a molecular graph network with frozen protein language model embeddings.

De novo small molecule generation that assembles drug-like graphs atom by atom, pretrained on cheap property proxies and finetuned per objective.

Self-supervised 3D molecular pretraining that corrupts and rebuilds tetrahedral local structures, then transfers to toxicity and affinity prediction.

Multimodal contrastive model aligning protein structure and sequence with ligand conformation and graph to retrieve binders without docking.

Chemical language model reading modified and cyclic peptides as SMILES, fine-tuned to predict passive membrane diffusion of macrocycles.

Text-to-text biological language model spanning molecules, proteins, and text, adding IUPAC names and multi-task instruction tuning to BioT5.

Multi-omics transformer generating transcriptomic, methylation and proteomic signatures for a given tissue, disease, age group, sex and compound.

Encoder-decoder chemical language model pretrained on 91 million PubChem SMILES, giving embeddings that decode back to valid molecules.

Structure-based molecular design that samples a quantum electron cloud in the protein pocket, then decodes it into ligands with a Llama-style model.

Molecular property prediction model pretrained by masked reconstruction of SMILES and graph, with disjoint masks that force cross-modal recovery.

Structure-based drug design that generates 3D ligands for a protein pocket entirely in the continuous parameter space of a Bayesian flow network.

Drug combination synergy prediction across cancer cell lines, from LLM text embeddings of drugs and cell lines rather than structures or expression.

Structure-based drug design that steers a frozen 3D molecule diffusion model with user-supplied pharmacophore points inside a protein pocket.

3D molecule generation that models atom coordinates and element types as distribution parameters updated by Bayesian inference, not by denoising.