Table 3 |.

Applications of diffusion models in bioinformatics

Applications	Tool name	Key function and strength	Input	Output	Diffusion targeta
Protein design and generation	ProteinSGM91	Inpaints plausible backbones/domains; generates native-like structures; allows precise and modular design	Inter-residue 6D feature maps	Full-atomistic structure	Inter-residue 6D feature maps
	FoldingDiff101	Mirrors native folding process; alleviates the need for equivariant networks; unconditionally generates realistic protein structures	6 consecutive backbone angles	Protein backbone structure	6 consecutive backbone angles
	DiffSDS102	Reduces computational complexity and cost; efficiently imposes geometric constraints; outperforms previous strong baselines	6 consecutive backbone angles	Masked protein backbone structure	6 consecutive backbone angles
	ProSSDG84	Operates at large scales; generates realistic proteins structures with sequences; allows interactive structure generation	Secondary structure; coarse constraints	All-atomistic protein structure	Coarse constraints
	Genie103	Dual representation for protein residues; designability and diversity	Oriented reference frames	Protein backbone structure	Oriented reference frames
	SMCDiff104	Efficiently samples scaffolds; samples conditioned on given motif; theoretically guarantees conditional samples	Molecular graph structure	Scaffold structure given input motif	Molecular graph structure
	RFdiffusion105	Generates diverse outputs; can be guided toward specific design objectives; explicitly models 3D structure	Sequence, predicted structure	Diverse, complex, functional protein	RF frames from a predicted structure
	FrameDiff106	Generates designable monomers and diverse protein backbones; does not require pretrained structure predictor	Molecular graph structure	Designable monomer backbone structure	Molecular graph structure
	Chroma107	Jointly models structures and sequences; sub-quadratic computational scaling; arbitrary conditional sampling	Protein graph structure	Proteins with desired functions	Protein graph structure
Small-molecule generation and drug design	CDGS108	Incorporates discrete graph structures; specialized graph noise prediction model; similarity-constrained molecule optimization pipeline	Graph structures and inherent features	Molecular graphs	Discrete graph structure
	EDM54	Equivariant to Euclidean transformations; operates on continuous and categorical features; admits likelihood computation	Atom coordinates, atom types	3D molecular graphs	Coordinates and categorical features
	DiffBridge109	Incorporates prior information; generates realistic molecules; uses physically informed diffusion bridges	Masked points in 3D Euclidean space	Realistic molecules or point cloud	Molecular graph structure
	DGSM110	Models local and long-range interactions; dynamically constructs graph structures; estimates gradient fields of logarithm density	2D molecular graphs	Stable 3D conformations	Molecular graph structure
	SDEGen111	Captures multimodal conformation distribution; quickly searches low-energy conformations and higher efficiency	Small molecules	Representative conformations	Encoding of graph structure
	DiffMD112	No intermediate variables; directly estimates gradient of log density; incorporates directions and velocities of atomic motions	3D coordinates, velocities, and invariant features	Molecule simulation trajectories	Atomic position coordinates
	DiffLinker113	Links arbitrary number of fragments; generates diverse and synthetically accessible molecules; conditions on protein pockets	Molecule structure	A molecule incorporating all the input fragments	Molecular graph structure
Protein–ligand interaction modelling	DiffBP114	Non-autoregressive generation; generates molecules with high affinity to target proteins and desirable drug properties	Protein–ligand structure	Protein–ligand structure given input protein	Protein graph structure
	DiffSBDD115	Generates diverse drug-like ligands; efficient in silico experiments; uses experimentally determined binding data	Protein–ligand structure	High-affinity ligands given protein pockets	Protein graph structure
	DiffDock116	Maps manifold to product space; provides confidence estimates; maintains precision on computationally folded structures	Protein–ligand structure	Ranked ligand poses, confidence scores	Ligand poses
	NeuralPLexer117	Repacks failed AlphaFold2 sites; enables end-to-end design; generalizes to ligand-unbound or predicted protein structure inputs	Protein backbone template and ligand molecular graphs	Full-atom protein–ligand structure	Contact maps, geometry prior
	NERE118	Benchmarks on protein–ligand and antibody–antigen dataset; outperforms other unsupervised methods	Protein–ligand complex	Binding affinity of the protein–ligand structure	Ligand atom coordinates
Cryo-EM data analysis	CryoDRGN98	Accurate data distribution sampling; fast latent space traversal; unlocks generative modelling tools	Single-particle cryo-EM imaging	High-quality 3D structure	Latent space of image embeds
Single-cell image and gene-expression analysis	DISPR99	Realistic 3D reconstructions; data augmentation tool in single-cell classification task; inverse biomedical problems	2D microscopy images as a prior	3D cell shape predictions	3D microscopy point cloud
	DEWAKSS100	Maintains cellular identity; preserves data variance; maintains cluster homogeneity	Gene-expression data	Single-cell genomics data	Gene-expression matrix

^aThe term ‘diffusion’ refers to the application of the forward and reverse diffusion processes on a specific data representation, that is, the target. Cryo-EM, cryogenic electron microscopy. RF, RosettaFold.