All Competitors

Chemical language model that translates IR, UV-Vis and 1H NMR spectra into SMILES structures, replacing the enumerate-and-filter CASE workflow.

Protein-ligand binding affinity prediction for Kd, Ki and IC50 from a pocket structure and a SMILES string, with no docked complex required.

Multimodal LLM aligning natural language, small molecules and proteins in any direction, turning prose design goals into molecules or enzymes.

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

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

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

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.

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.

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.

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

Blind protein-ligand docking that transfers to binding domains absent from training, scoring 22.6% top-1 on DockGen and 50% on PoseBusters.

All-atom 3D molecular foundation model pretrained across small molecules, proteins, and complexes with an E(3)-equivariant denoising objective.

Compound-protein interaction prediction coupling a chemical language model to a protein language model via a cross-attention block.

Protein-ligand binding affinity prediction from an amino acid sequence and a ligand SMILES string, with no structure, docked pose, or pocket needed.

Structure-based drug design diffusion model that re-extracts the essential binding subcomplex from a pocket at every step of 3D ligand generation.